IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning

Citation: Nancy, A.A.; Ravindran, D.; Raj Vincent, P.M.D.; Srinivasan, K.; Gutierrez Reina, D. IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning. Electronics 2022,11, 2292. https://doi.org/10.3390/ electronics11152292 Academic Editors: Francisco Luna-Perejón, Lourdes Miró Amarante and Francisco Gómez-Rodríguez Received: 20 June 2022 Accepted: 19 July 2022 Published: 22 July 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Article IoT-Cloud-Based Smart Healthcare Monitoring System for Heart Disease Prediction via Deep Learning A Angel Nancy 1, Dakshanamoorthy Ravindran 1, P M Durai Raj Vincent 2, Kathiravan Srinivasan 3 and Daniel Gutierrez Reina 4,* 1Department of Computer Science, St. Joseph’s College (Autonomous), Bharathidasan University, Tiruchirappalli 620002, India; [email protected] (A.A.N.); [email protected] (D.R.) 2School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India; [email protected] 3School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India; [email protected] 4Electronic Engineering Department, University of Seville, 41004 Sevilla, Spain *Correspondence: dgutierr[email protected] Abstract: The Internet of Things confers seamless connectivity between people and objects, and its confluence with the Cloud improves our lives. Predictive analytics in the medical domain can help turn a reactive healthcare strategy into a proactive one, with advanced artificial intelligence and machine learning approaches permeating the healthcare industry. As the subfield of ML, deep learning possesses the transformative potential for accurately analysing vast data at exceptional speeds, eliciting intelligent insights, and efficiently solving intricate issues. The accurate and timely prediction of diseases is crucial in ensuring preventive care alongside early intervention for people at risk. With the widespread adoption of electronic clinical records, creating prediction models with enhanced accuracy is key to harnessing recurrent neural network variants of deep learning possessing the ability to manage sequential time-series data. The proposed system acquires data from IoT devices, and the electronic clinical data stored on the cloud pertaining to patient history are subjected to predictive analytics. The smart healthcare system for monitoring and accurately predicting heart disease risk built around Bi-LSTM (bidirectional long short-term memory) showcases an accuracy of 98.86%, a precision of 98.9%, a sensitivity of 98.8%, a specificity of 98.89%, and an F-measure of 98.86%, which are much better than the existing smart heart disease prediction systems. Keywords: cloud computing; Internet of Things; healthcare; predictive analytics; recurrent neural network 1. Introduction Human evolution has unfolded in synergy with science and technology evolution. Information and communication technology (ICT) advancements have laid the foundation for innovative solutions in diverse industry domains, including healthcare, agriculture, transportation, and logistics, among others. The Internet of Things (IoT) is a substantive driving force to ICT technological advancement, leading prospective sectors down the road of automation alongside decentralized intelligence [ 1 ]. The IoT evolves incessantly and impacts every facet of our life while resembling a living entity. From household appliances to robots in factories, the IoT connects data, people, things/objects, and processes. Meanwhile, cloud computing (CC) delivers on-demand elastic services with virtually unlimited computation and storage capability [ 2 ]. Despite being unique and independent in their respective evolution, cloud computing and IoT aspects complement one another. Eventually, the two technologies converged in recent years, and the confluence became known as a Cloud–IoT paradigm [ 3 , 4 ], offering tremendous prospects for driving new innovative services and applications. Electronics 2022,11, 2292. https://doi.org/10.3390/electronics11152292 https://www.mdpi.com/journal/electronics Electronics 2022,11, 2292 2 of 19 Healthcare-related applications have pushing for innovations in science and technology since information technology applications began to remotely acquire, track and control the status of patients. Thus, IoT drives recent innovations in healthcare and revolutionizes them by acquiring the physiological data of patients through sensor networks and wearable devices [ 5 ]. The Cloud–IoT harnesses the Cloud’s tremendous potential for the storage and processing of an enormous volume of clinical records of patients including sensor data from medical IoT for healthcare analytics. Analytics ensues the systematic quantitative and qualitative analysis of concerned data for efficient decision making, while predictive analytics stems from advanced analytics aiming to elicit the prognosis of future occurrences using the available data [ 6 ]. The analytics in healthcare is harnessed for clinical decision support, predictive risk assessment, and remote health monitoring, among other crucial tasks. Predicting and lowering risk based on current and past patient data are a big part of medicine. The integration of humongous data from disparate sources comprising electronic health records, medical imaging, screening results, and administrative information warranting swift decisions is effectively tackled by healthcare analytics [ 7 ]. Clinicians must often make decisions with a high degree of uncertainty; however, with the headway of predictive analytics in healthcare, those decisions will be more informed than ever. These cutting-edge predictive analytics approaches help identify trouble early on, avoid complication risks, improve chronic illness management, evade hospital readmission, receive medical research aid, and minimize overhead expenses. Predictive analytics in healthcare deploys diverse techniques from conventional linear models to advanced algorithms of artificial intelligence (AI) and machine learning (ML) [ 8 ]. Deep learning (DL), a subfield of ML, is sufficiently reliable and robust to automatically handle and learn from a huge amount of complex healthcare data and offers actionable insights and solutions to intricate problems. Its deployment to a wide variety of medical applications has surpassed the results of traditional models. Specifically, the recurrent neural network (RNN) [ 9 ] is competent at managing the long-term dependencies of input data and has grown prominent in the study of temporal events concerning time-sequential applications. 1.1. Motivation Predictive analytics is proving its worth, not just in the hospital environment, but also at home by remote monitoring and keeping patients from relapsing into the need for acute treatment. Predictive analytics aid in the diagnosis, prognosis, and therapy at every stage of a patient’s treatment [ 10 ]. It also helps in designing the treatment course, providing clinical decision support, decreasing adverse occurrences, and enhancing the overall care quality while lowering healthcare costs. Moreover, the personalized healthcare model shifts from treating patients as numbers to treating them as individuals, customizing treatment to their unique medical history, environment, social risk factors, genetics, and biochemistry, among other things [ 11 ]—rather than depending on demographic statistics that do not apply to everyone. It tenders real-time clinical decision assistance at the point of treatment, allowing for the most efficient delivery of individualized healthcare [ 12 ]. With deadly diseases, spotting them early on and detecting any possible deterioration in the patients’ condition before occurrence can significantly improve the odds of an effective treatment. The diseases affecting the heart and its related blood vessels are all classified as cardiovascular diseases (CVDs). These include arrhythmia, coronary artery disease, congenital heart disease, valve disease, aortic disease, heart failure, peripheral artery disease, pericardial disease, heart valve disease, cerebrovascular disease, rheumatic heart disease, deep vein thrombosis, cardiomyopathy, myocarditis, atrial fibrillation, ischemic heart disease, and stroke [13–15]. The most prominent cause of global mortality is cardiovascular diseases (CVDs), claiming the lives of an estimated 17.9 million individuals and accounting for 32% of all fatalities worldwide [ 16 ]. Heart attacks and strokes cause four out of every five CVD Electronics 2022,11, 2292 3 of 19 fatalities, which are 85% of all CVD mortalities, with one-third occurring before age 70. Identifying individuals at risk for CVDs and ensuring that they receive proper therapy can help avert untimely deaths. This is where the predictive algorithms powered by AI and ML come into play alongside the Internet of Things, as these are adept at managing massive and diverse data. Pattern classification, as a pattern recognition task, is a crucial supervised learning paradigm for identifying and classifying disease patterns in the medical field [ 17 ]. The researchers working on classification algorithms concerning heart disease strive to achieve the maximum classification accuracy possible as patients’ lives are at stake. Many individuals are at risk of heart disease due to long-term conditions such as persisting high blood pressure. With the increase in the aging population across the globe, most of them are diagnosed with chronic heart conditions. This warrants the continuous real-time monitoring of individuals at in-home care and the patients in treatment within hospital premises, entailing timely treatment upon the fluctuation of vital signs. The prolonged tracking of health conditions in the elderly helps minimize hospitalization cost and enhance the quality of life, but conventional methods are tedious and tiring. This necessitates efficient facilities to mitigate the overwhelming workload of clinicians and hospital staff while minimizing the cost of health monitoring. The pervasive nature of IoT has incited the proliferation of smart, interconnected devices and wearables with sensors, thereby facilitating remote patient monitoring pertaining to heart disease. The IoT for healthcare monitoring includes smart health watches, wearable blood pressure monitors, and wearable ECG monitors equipped with medical sensors. Thus, the healthcare IoT acquires vital patient data and transmits them to the Cloud for storage and complex deep learning analytics along with prior electronic clinical records for accurate heart risk diagnostics. These IoT devices can swiftly notify the clinicians and caretakers of the patient’s condition. This enables clinicians to better make timely decisions for individuals as well as the population at large by estimating patients’ chance of developing a specific heart disease, their prognosis for the given condition, and the corresponding treatment. 1.2. Contribution The pivotal outcomes of this research initiative are listed as follows: 1. The data collected from IoT sensors pertaining to heart disease risk prediction are subject to the data pre-processing tasks of data cleaning and data filtering at the Cloud layer; 2. The ensuing data are sent to the fuzzy information system (FIS) for the initial classification task; 3. Finally, the proposed Bi-LSTM model is used to accurately predict the risk of heart disease in patients. The remaining sections of this article are organized into related work, methodology, experimental setup, performance assessment, experimental results and discussion, comparative analysis, future directions, and conclusions. 1.3. Related Work In recent times, diverse systems for heart disease prediction have been propounded. For enhancing heart disease risk prediction accuracy, the deployment of several ensemble classifiers displays an accuracy of 85.4% [ 18 ]. A model for diagnosing heart disease diagnosis that combines rough sets-based attribute reduction involving the chaos firefly algorithm with an interval type-2 fuzzy logic system showcases an accuracy of 86% [ 19 ]. A machine learning hybrid model to predict heart disease [ 20 ] by combining random forest (RM) with linear method (LM) approaches exhibits a performance accuracy of 88.7%. An integrated decision support system for predicting the risk of heart failure, which combines a fuzzy analytic hierarchy process and artificial neural network for feature weighting and classification tasks, respectively, achieves 91.0% accuracy [ 21 ]. A smart system for diagnosing heart disease deploying a χ2 statistical model and a deep neural network for Electronics 2022,11, 2292 4 of 19 feature refinement and classification tasks, respectively, is proposed. The model attains an accuracy, specificity, and sensitivity of 91.57%, 93.12%, and 89.78%, respectively [22]. An adaptive weighted fuzzy rule-based system for assessing the heart disease risk level is presented. This automatic diagnostic system of a fuzzy model based on a genetic algorithm along with a deployed modified dynamic multi-swarm particle optimization approach shows an accuracy of 92.3% [ 23 ]. A heart disease identification model deploying algorithms of univariate and relief feature selection alongside a decision tree for classification achieves an accuracy of 92.8% [24]. A coronary artery disease prediction model with a neuro-fuzzy medical decision support system is presented. This system involves an artificial neural network and an adaptive neuro-fuzzy inference system, which displays an accuracy, sensitivity, specificity, and precision of 94.15%, 91.44%, 95.59%, and 92.61%, respectively [ 25 ]. A system for automatically predicting heart disease is proposed, which deploys cluster-based Bi-LSTM (bidirectional long short-term memory). When tested with the UCI dataset, this model exhibits an accuracy of 94.78% [26]. An expert system for diagnosing heart disease by combining fuzzy rules and deep neural networks is presented, showing an overall accuracy of 96.5% [ 27 ]. A method that integrates CNN with deep learning algorithms referred to as CardioHelp is introduced, which uses CNN for early heart failure prediction involving a temporal model. This approach outperforms other state-of-the-art methods, with a 97% accuracy rate [28]. An IoT-based hybrid system for cardiovascular disease prediction is offered, including sequential forward selection (SFS) as the feature selection technique and a random forest for classification. This system recommends physical as well as dietary plans to patients in function of their age and gender, showing 98% accuracy compared to other heuristic model recommender systems [ 29 ]. A model capable of handling medical data from multiple sensors involving an ensemble classifier—Kernel random forest [ 30 ]—shows 98% accuracy when deployed on a heart disease dataset. A new IoT framework based on deep convolutional neural networks, which are connected to a wearable sensor that measures the blood pressure and ECG of a patient, is suggested. When compared to logistic regression and existing deep learning neural networks, this technique performs better with 98.2% accuracy [ 31 ]. A smart system predicting the risk of heart disease [ 32 ] from data acquired by wearable sensors and patient medical history, based on the ensemble deep learning model Logitboost along with feature fusion technique, was presented. The system shows 98.5% accuracy in heart disease diagnoses while automatically recommending dietary plans in function of the health condition. A model for predicting heart disease is presented by combining the method of embedded feature selection involving the LinearSVC algorithm with deep neural networks. This system achieves an accuracy, recall, precision, and F-measure of 98.56%, 99.35%, 97.84%, and 98.3%, respectively, when evaluated with the heart disease dataset [33]. The aforementioned state-of-the-art works pertaining to heart disease risk diagnosis harnessing the UCI heart disease dataset predominantly avail statistical and machine learning algorithms and methods. The classification accuracy shown by the existing methods has the possibility of further enhancement when deep learning approaches are emphasized. Moreover, utilizing the fuzzy systems alongside recurrent neural network algorithms has the potential to offer better outcomes. The ensuing sections elaborate on the proposed fuzzy-based recurrent neural network model for accurate heart disease risk prediction. 2. Materials and Methods IoT technology acts as the critical acquisition component for innumerable real-time applications that promote object–individual interaction. The massive amount of data generated by IoT devices poses a significant challenge to the healthcare system pertaining to the processing, storage, and management of data. Electronics 2022,11, 2292 5 of 19 The proposed smart healthcare system for heart disease risk prediction includes modules such as (1) the data acquisition/collection layer; (2) data pre-processing; and (3) the disease prediction layer, which is depicted in Figure 1. Electronics 2022, 11, x FOR PEER REVIEW 5 of 20 proposed fuzzy-based recurrent neural network model for accurate heart disease risk prediction. 2. Materials and Methods IoT technology acts as the critical acquisition component for innumerable real-time applications that promote object–individual interaction. The massive amount of data generated by IoT devices poses a significant challenge to the healthcare system pertaining to the processing, storage, and management of data. The proposed smart healthcare system for heart disease risk prediction includes modules such as (1) the data acquisition/collection layer; (2) data pre-processing; and (3) the disease prediction layer, which is depicted in Figure 1. Figure 1. Heart disease risk prediction system—block diagram. 2.1. Data Acquisition/Collection Layer The propounded healthcare system acquires data from two primary data sources. The physiological data of patients such as their blood pressure (BP), heart rate, blood sugar/glucose level, respiration rate, blood oxygen, cholesterol level, activity, electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) are gathered from the patient’s routine health monitoring. These data are transmitted through Bluetooth/Zigbee to related remote gateway devices and then to the cloud data center, where data pre-processing and disease prediction takes place. The other data source is the electronic clinical data (ECD), which comprise the patient’s medical history (including their history of smoking and diabetes), observation reports, and comprehensive clinical (lab) reports which offer valuable information on disease prediction and are stored in a cloud database. 2.1.1. Dataset For the experiment, to detect the presence of heart disease from heart patient data, the Cleveland and Hungarian dataset from the UCI machine learning repository are considered. The proposed algorithm was deployed on a heart dataset that includes 14 attributes, as depicted in Table 1. Table 1. Attribute description. Figure 1. Heart disease risk prediction system—block diagram. 2.1. Data Acquisition/Collection Layer The propounded healthcare system acquires data from two primary data sources. The physiological data of patients such as their blood pressure (BP), heart rate, blood sugar/glucose level, respiration rate, blood oxygen, cholesterol level, activity, electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) are gathered from the patient’s routine health monitoring. These data are transmitted through Bluetooth/Zigbee to related remote gateway devices and then to the cloud data center, where data pre-processing and disease prediction takes place. The other data source is the electronic clinical data (ECD), which comprise the patient’s medical history (including their history of smoking and diabetes), observation reports, and comprehensive clinical (lab) reports which offer valuable information on disease prediction and are stored in a cloud database. Dataset For the experiment, to detect the presence of heart disease from heart patient data, the Cleveland and Hungarian dataset from the UCI machine learning repository are considered. The proposed algorithm was deployed on a heart dataset that includes 14 attributes, as depicted in Table 1. Electronics 2022,11, 2292 6 of 19 Table 1. Attribute description. S. No. Attribute Description 1 age Age of patient in years 2 sex 1 = male; 0 = female 3 cp Type of chest pain (1 = angina, 2 = atypical form of angina, 3 = non-angina, 4 = no symptoms of angina) 4 trestbps Resting blood pressure 5 chol Cholesterol value 6 fbs Fasting blood sugar value >120 mg/dL (1 = true and 0 = false) 7 restecg Value of ECG at rest (0 = normal, 1 = abnormal (ST-T wave), 2 = definite ventricular) 8 thalach Maximum heart rate recorded 9 exang Exercise induced angina (1 = yes; 0 = no) 10 oldpeak Exercise induced ST Depression 11 slope Slope of T segment peak exercise (1 = unsloping, 2 = flat, and 3 = down sloping) 12 ca Major vessels number (0–3) coloured by fluoroscopy 13 thal 3 = normal; 6 = fixed defect; 7 = reversable defect 14 target The predicted heart disease status (0 = No and 1 = Yes) 2.2. Data Pre-Processing Layer Data pre-processing has become a requisite for ML algorithm deployment as realworld data are prone to being inconsistent, incomplete, and noisy. Efficient heart disease prediction from the heart disease dataset requires missing data handling, normalization, and feature selection. Data acquired from wearable sensors are impacted due to signal aberrations, such as missing values and noise, causing havoc in the case of heart disease prediction, compromising the prediction accuracy, or yielding an erroneous result. We utilize a well-known technique to filter the data known as Kalman filtering [32,34], which effectively eliminates duplicate records, noise, and discrepancies from the data. Owing to its simple form, it requires low computational power [ 35 ]. This unsupervised filtering algorithm is specialized to handle vast real-time sensor data and furnish values closer to that of the actual values from the sensor without noise [ 36 ]. In addition to this, we use two other unsupervised filters in the data filtering stage: removing useless and replace missing values [ 32 ]. With another 90% of maximum variance, the first filter eliminates irrelevant attributes. The second filter substitutes the mean as well as median values of the existing data for any values missing in the structured dataset. Fuzzy Inference System The term fuzzy refers to something as inexplicit or vague, and the fuzzy system is inspired by the requisite to model inherently vague real-world events [ 37 ]. The standard fuzzy system is characterized by four components, namely a fuzzifier, an inference engine, a knowledge base, and a defuzzifier. The inputs to a typical fuzzy system can be crisp data (numeric) and linguistic values (fuzzy sets). In the case of a crisp input, the fuzzifier assigns to it the applicable fuzzy set and this process is known as fuzzification. Then, the inference engine accomplishes mapping of the input variable values to the linguistic values of the output variable through a suitable approximate reasoning method with expert knowledge indicated by a set of fuzzy conditional rules in the knowledge base. The knowledge base entails the application of domain knowledge which can be divided into a database and a rule base. The database comprises linguistic control rules, and the rule base includes domain expert knowledge. In addition to linguistic values, if numeric data output is needed, then defuzzification assigns crisp data to the resulting fuzzy set. The classification of heart disease risk based on patients’ health data is performed using a fuzzy inference system (FIS), and the algorithm is presented as Algorithm 1. Electronics 2022,11, 2292 7 of 19 Algorithm 1: Classification of patients’ health data using FIS. Step 1. The inputs and the respective member functions µ1determines the fuzzy system Step 2. Ascertain heart disease risk state using µ1(ECG1), µ1(MaxHeartRate1), µ1(BloodPressure1) as µ1(normal) or µ1(low) or µ1(high) Step 3. If Health risk state = µ1(high) 3.1 Send alert to GDusing SPARK as RTA 3.2 Store Health risk state of the Puid in CS Step 4. Otherwise send Health risk state of the Puid to CS Step 5. End the process The inputs for maximum heart rate, ECG, and blood pressure, are created and member function are fed, which are fuzzified into fuzzy sets using a fuzzy value range. Figure 2 presents the working of FIS for heart disease risk prediction. Electronics 2022, 11, x FOR PEER REVIEW 7 of 20 μ1 (BloodPressure1) as μ1 (normal) or μ1 (low) or μ1 (high) Step 3. If Health risk state = μ1 (high) 3.1 Send alert to GD using SPARK as RTA 3.2 Store Health risk state of the Puid in CS Step 4. Otherwise send Health risk state of the Puid to CS Step 5. End the process The inputs for maximum heart rate, ECG, and blood pressure, are created and member function are fed, which are fuzzified into fuzzy sets using a fuzzy value range. Figure 2 presents the working of FIS for heart disease risk prediction. Figure 2. FIS for heart disease risk prediction. The fuzzy sets thus created are given as input to the FIS for classifying patients based on their health data. Table 2 depicts the linguistic variables and their corresponding fuzzy set of the FIS. Table 3 shows the member function and range for the blood pressure variable. Table 2. FIS—linguistic variable and fuzzy set. Linguistic Variable Fuzzy Set Max Heart Rate {Low risk, Normal, High risk} ECG {Low risk, Normal, High risk} Blood Pressure {Low risk, Normal, High risk} Table 3. Blood pressure—member function and its range. Member Function Range Low [40/90–70/100] Normal [70/110–80/120] High [90/130 and above] The input variable value is mapped into the output variable’s linguistic values through a suitable approximate reasoning method given as fuzzy conditional rules in the knowledge base. The results are classified in function of these fuzzy rules in the rule base along with corresponding member functions. The notification is sent regarding high-risk patients, and the overall patient risk status is stored in the cloud for future analysis. The data of patients classified as high risk for heart disease are subjected to further analysis in the ensuing prediction layer. 2.3. Data Prediction Layer Sequence prediction challenges have existed for a long time and are often regarded as one of the most challenging problems in the data science sector to tackle. 2.3.1. RNN Figure 2. FIS for heart disease risk prediction. The fuzzy sets thus created are given as input to the FIS for classifying patients based on their health data. Table 2depicts the linguistic variables and their corresponding fuzzy set of the FIS. Table 3shows the member function and range for the blood pressure variable. Table 2. FIS—linguistic variable and fuzzy set. Linguistic Variable Fuzzy Set Max Heart Rate {Low risk, Normal, High risk} ECG {Low risk, Normal, High risk} Blood Pressure {Low risk, Normal, High risk} Table 3. Blood pressure—member function and its range. Member Function Range Low [40/90–70/100] Normal [70/110–80/120] High [90/130 and above] The input variable value is mapped into the output variable’s linguistic values through a suitable approximate reasoning method given as fuzzy conditional rules in the knowledge base. The results are classified in function of these fuzzy rules in the rule base along with corresponding member functions. The notification is sent regarding high-risk patients, and the overall patient risk status is stored in the cloud for future analysis. The data of patients classified as high risk for heart disease are subjected to further analysis in the ensuing prediction layer. 2.3. Data Prediction Layer Sequence prediction challenges have existed for a long time and are often regarded as one of the most challenging problems in the data science sector to tackle. Electronics 2022,11, 2292 8 of 19 2.3.1. RNN Deep learning algorithms were extensively researched and widely deployed in recent years for extracting information from several types of data. Neural networks can learn representations and uncover previously unknown structures. Numerous deep learning architectures, namely the conventional neural network, deep neural network, and recurrent neural network take into account diverse aspects of input data [ 38 ]. In most cases, CNN and DNN are inept in coping with the input’s temporal information. RNNs prevail in domains dealing with sequential input, such as text, audio, or video. A cyclic connection is a common component of the RNN design which allows the updating of its current state depending on the current input and previous states [ 39 ]. The RNNs include the hidden or recurrent layers, which consist of recurrent cells. The states of the recurrent cells are impacted by the current input that has feedback connections and past states. Different RNNs can be formed by organizing recurrent layers into different architectures. Thus, the recurrent cell, as well as the network architecture, distinguish RNNs. The capability of RNNs is influenced by varying cells and their inner connections. In some situations, these networks, such as complete RNNs and selective RNNs, made up of conventional recurrent units (sigma cells and tanh cells), have showcased phenomenal success. However, the RNNs with standard recurrent cells are inadept at managing longterm dependencies as it is daunting to identify the interconnecting information with a considerable gap between the related input data. 2.3.2. LSTM Long short-term memory (LSTM) has been proposed to contend with “long-term dependency” as the outcome of exhaustive research on RNNs, aimed at sequence learning. Long-range interdependence and nonlinear dynamics can be captured using LSTMs [ 40 ] as it functions as the refined version of RNN, with the hidden layer unit of the memory cells in the place of recurrent units. Figure 3illustrates the generic LSTM model. Electronics 2022, 11, x FOR PEER REVIEW 9 of 20 where f, i, o, and c are the forget gate, input gate, output gate, and cell activation vectors, respectively, while W(f, i, o, c) and b(f, i, o, c) correspond to their weight matrices and bias vectors, respectively, and h represents hidden value. The term 𝑥𝑡 refers to the input of the memory cell at time t while 𝑐𝑡 and 𝑐𝑡  denote the current and previous memory cell units. Figure 3. Generic LSTM model. 2.3.3. Proposed Bi-LSTM Model The constraint of the LSTM cell is that it can act on prior content but not on the future one. Bidirectional recurrent neural networks consisting of two distinct LSTM hidden layers with comparable output in opposing directions were put forth. Previous and future information is used in the output layer using this approach. In Bi-LSTM, an input sequence 𝑋 = (𝑋1,𝑋2,…,𝑋𝑛) is computed in the forward direction as ℎ𝑖 󰇍 󰇍 󰇍 = (ℎ1 󰇍 󰇍 󰇍 󰇍 ,ℎ2 󰇍 󰇍 󰇍 󰇍 ,⋯,ℎ𝑛 󰇍 󰇍 󰇍 󰇍 󰇍 󰇍 󰇍 ) and in the backward direction as ℎ𝑡  󰇍 󰇍 󰇍 = (ℎ1  󰇍 󰇍 󰇍 󰇍 ,ℎ2  󰇍 󰇍 󰇍 󰇍 ,…,ℎ𝑛  󰇍 󰇍 󰇍 󰇍 ). The final output of this cell 𝑦𝑡 is created by both ℎ𝑖 󰇍 󰇍 󰇍 and ℎ𝑡  󰇍 󰇍 󰇍 , and the final output sequence is 𝑦 = (𝑦1,𝑦2,…𝑦𝑡…,𝑦𝑛). In deep networks, the chosen activation function profoundly influences the training dynamics along with the task performance. The activation function proposed by the Google Brain Team [41], Swish, stated as f(x) = x.sigmoid(βx), was chosen for the prediction model. To address the cell divergence issue of the generic model, a tanh activation function is included in the cell propagation, and a leaky rectified linear unit (Leaky ReLU) is inserted after output gating. These collectively show the reduced prediction oscillation and eliminated negative outputs. Figure 4 shows the LSTM cell structure and Bi-LSTM of the proposed model. 𝑓𝑡= swish(𝑊 𝑓𝑥𝑡+ 𝑊ℎ𝑓ℎ𝑡−1 + 𝑏𝑓) (7) 𝑖𝑡= swish(𝑊𝑖𝑥𝑡+ 𝑊ℎ𝑖ℎ𝑡−1 + 𝑏𝑖) (8) 𝑜𝑡= swish(𝑊 𝑜𝑥𝑡+ 𝑊ℎ𝑜ℎ𝑡−1 + 𝑏𝑜) (9) 𝑐𝑡  = tanh(𝑊 𝑐𝑥𝑡+ 𝑊ℎ𝑐ℎ𝑡−1)+ 𝑏𝑐 (10) 𝑐𝑡= 𝑓𝑡∗ 𝑐𝑡−1 + 𝑖𝑡∗ 𝑐𝑡  + tanh (11) ℎ𝑡= 𝑜𝑡∗ 𝑐𝑡 (12) 𝑦𝑡= 𝑜𝑡∗ 𝑐𝑡∗ Leaky ReLU (13) Figure 3. Generic LSTM model. The memory cells enable retaining and output information, thereby facilitating the learning of long-term temporal correlations. This includes self-connections that retain the network temporal state and are regulated by three gates: input gate, output gate, and the forget gate. Gating is a process that determines the function of each memory cell in LSTMs. When the gate is activated, the LSTM updates its cell state. The input and output gates govern the flow of memory cell inputs and outputs into the remainder of the network. A forget gate was also introduced to the memory cell, which passes the high-weighted output information from one neuron to the next. The information retained in memory is determined by the input unit’s high activation level; if it is high, the memory cell stores the information. Furthermore, a highly activated input unit will transfer information to the following neuron. Alternatively, high-weighted input data are stored in memory cells. The Electronics 2022,11, 2292 9 of 19 LSTM units’ activation is similarly determined to RNNs. LSTM network involves mapping between input and output sequence, i.e., X= (X1,X2, . . . , Xn)and Y= (Y1,Y2, . . . , Yn). ft=σWfxt+Wh f ht−1+bf(1) it=σ(Wixt+Whiht−1+bi)(2) ot=σ(Woxt+Whoht−1+bo)(3) e ct=tanh(Wcxt+Whcht−1)+bc(4) ct=ft∗ct−1+it∗e ct(5) ht=ot∗tan h (ct)(6) where f,i,o, and care the forget gate, input gate, output gate, and cell activation vectors, respectively, while W(f,i,o,c) and b(f,i,o,c) correspond to their weight matrices and bias vectors, respectively, and h represents hidden value. The term xt refers to the input of the memory cell at time twhile ctand e ctdenote the current and previous memory cell units. 2.3.3. Proposed Bi-LSTM Model The constraint of the LSTM cell is that it can act on prior content but not on the future one. Bidirectional recurrent neural networks consisting of two distinct LSTM hidden layers with comparable output in opposing directions were put forth. Previous and future information is used in the output layer using this approach. In Bi-LSTM, an input sequence X= (X1 , X2 , . . . , Xn) is computed in the forward direction as → hi= ( → h1 , → h2 , · · · , → hn ) and in the backward direction as ( ht= ( ( h1 , ( h2 , · · · , ( hn ). The final output of this cell yt is created by both → hiand ( htand the final output sequence is y= (y1,y2, . . . yt. . . , yn). In deep networks, the chosen activation function profoundly influences the training dynamics along with the task performance. The activation function proposed by the Google Brain Team [ 41 ], Swish, stated as f(x) = x.sigmoid( β x), was chosen for the prediction model. To address the cell divergence issue of the generic model, a tanh activation function is included in the cell propagation, and a leaky rectified linear unit (Leaky ReLU) is inserted after output gating. These collectively show the reduced prediction oscillation and eliminated negative outputs. Figure 4shows the LSTM cell structure and Bi-LSTM of the proposed model. ft=swishWfxt+Wh f ht−1+bf(7) it=swish(Wixt+Whiht−1+bi)(8) ot=swish(Woxt+Whoht−1+bo)(9) e ct=tanh(Wcxt+Whcht−1)+bc(10) ct=ft∗ct−1+it∗e ct+tan h (11) ht=ot∗ct(12) yt=ot∗ct∗Leaky ReLU (13) where f,i,o, and care the forget gate, input gate, output gate, and cell activation vectors, respectively, while W(f,i,o,c) and b(f,i,o,c) correspond to their weight matrices and bias vectors, respectively, and h represents hidden value. The term xt refers to the input of the memory cell at time twhile ct , e ct , and yt denote the current and previous memory cell units as well as the final output, respectively. Electronics 2022,11, 2292 16 of 19 Electronics 2022, 11, x FOR PEER REVIEW 17 of 20 Figure 11. Comparison with state-of-the-art systems. The results of the comparison with the related state-of-the-art heart disease predictive systems reveal that the proposed system’s performance surpasses that of the existing systems. Major IoT-driven tasks for real-time smart systems involving healthcare warrant rapid processing as such applications are delay and context-sensitive. The escalation in the number of IoT devices and the upsurge in the data generated by the smart devices has resulted in immense data traffic resulting in extensive bandwidth utilization and service difficulties. As the Cloud–IoT model suffers from limitations such as latency, connectivity, and bandwidth utilization, the cloud computing model seems inadequate to manage these challenges solely due to its centralized model [43–47]. These shortcomings set the stage for decentralized models of edge computing (EC) and fog computing (FC), wherein computation and storage can be handled at the edge nodes closer to the data source. These newer computing technologies complement the Cloud and serve as an extension to it while enabling artificial intelligence tasks at the edge nodes. This hierarchical edge– fog–cloud model considerably reduces the delay constraints by efficiently handling the humongous data acquired by the IoT devices while mitigating latency. Thus, the proposed cloud-based prediction system can be deployed at the fog/edge layers in the future to overcome the Cloud’s intrinsic constraints, such as increased latency and bandwidth use, while managing the surge in IoT data. 80 84 88 92 96 100 Accuracy (%) Latha and Jeeva Long et al. Mohan et al. Samuel et al. Ali et al. Paul et al. Ahmed et al. Kishore and Jayanthi Dileep et al. Pam et al. Mehmood et al. Jabeen et al. Muzammal et al. Khan et al. Ali F et al. Zhang et al. Proposed Figure 11. Comparison with state-of-the-art systems. 7. Conclusions In this research initiative, an IoT–Cloud-based smart healthcare system for heart disease risk prediction is proposed, and the fuzzy inference system (FIS) and the recurrent neural network’s bidirectional LSTM are harnessed for the predictive task. The proposed system’s accuracy, precision, sensitivity, specificity, and F1-score are 98.85%, 98.9%, 98.8%, 98.89%, and 98.85%, respectively, outperforming other state-of-the-art heart disease prediction models. This is just one facet of the healthcare research being done performed with predictive analytics, with a huge potential of deep learning models yet to uncover. The model can be enhanced to automatically elicit a personalized diet and exercise recommendations to individuals as per their health condition and heart specialist advice. The proposed smart heart disease prediction system utilizes IoT devices for data acquisition, and other predominant tasks are reserved for the Cloud. In the future, this work can be extended to include fog/edge computing, wherein time-critical analytical tasks can be accomplished at the fog/edge layers to overcome the inherent limitations of the Cloud, such as increased latency and bandwidth utilization while handling IoT data upsurge [ 48 – 51 ]. The efficacy of the healthcare domain can be revolutionized with precise and timely disease predictions alongside rapid responses and agile decision-making by clinicians, which will improve the overall quality-of-service when fog/edge computing is involved. Electronics 2022,11, 2292 17 of 19 Author Contributions: Conceptualization, A.A.N., D.R., and K.S.; methodology, A.A.N., D.R., and K.S.; software, A.A.N.; validation, P.M.D.R.V., K.S., and D.G.R.; formal analysis, A.A.N.; investigation, A.A.N.; resources, K.S. and D.G.R.; data curation, A.A.N.; writing—original draft preparation, A.A.N.; writing—review and editing, A.A.N., D.R., P.M.D.R.V., K.S., and D.G.R.; visualization, A.A.N. and K.S.; supervision, D.R.; project administration, D.G.R.; funding acquisition, D.G.R. All authors have read and agreed to the published version of the manuscript. Funding: No funding was received for this study. Data Availability Statement: The original contributions generated for this study are included in the article; further inquiries can be directed to the corresponding author. Conflicts of Interest: The authors declare no conflict of interest. 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