Systemic lupus erythematosus: how machine learning can help distinguish between infections and flares

Citation: Usategui, I.; Arroyo, Y.; Torres, A.M.; Barbado, J.; Mateo, J. Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares. Bioengineering 2024,11, 90. https://doi.org/10.3390/ bioengineering11010090 Academic Editor: Alan Wang Received: 30 November 2023 Revised: 7 January 2024 Accepted: 15 January 2024 Published: 17 January 2024 Copyright: © 2024 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/). bioengineering Article Systemic Lupus Erythematosus: How Machine Learning Can Help Distinguish between Infections and Flares Iciar Usategui 1, Yoel Arroyo 2, Ana María Torres 3,4, Julia Barbado 5and Jorge Mateo 3,4,* 1Department of Internal Medicine, Hospital Clínico Universitario, 47005 Valladolid, Spain 2Department of Technologies and Information Systems, Faculty of Social Sciences and Information Technologies, Universidad de Castilla-La Mancha (UCLM), 45600 Talavera de la Reina, Spain 3Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha (UCLM), 16071 Cuenca, Spain 4Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain 5Department of Internal Medicine, Hospital Universitario Río Hortega, 47012 Valladolid, Spain *Correspondence: jor[email protected] Abstract: Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune ailment that impacts multiple bodily systems and manifests with varied clinical manifestations. Early detection is considered the most effective way to save patients’ lives, but detecting severe SLE activity in its early stages is proving to be a formidable challenge. Consequently, this work advocates the use of Machine Learning (ML) algorithms for the diagnosis of SLE flares in the context of infections. In the pursuit of this research, the Random Forest (RF) method has been employed due to its performance attributes. With RF, our objective is to uncover patterns within the patient data. Multiple ML techniques have been scrutinized within this investigation. The proposed system exhibited around a 7.49% enhancement in accuracy when compared to k-Nearest Neighbors (KNN) algorithm. In contrast, the Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), Decision Trees (DT) and Linear Regression (LR) methods demonstrated inferior performance, with respective values around 81%, 78%, 84% and 69%. It is noteworthy that the proposed method displayed a superior area under the curve (AUC) and balanced accuracy (both around 94%) in comparison to other ML approaches. These outcomes underscore the feasibility of crafting an automated diagnostic support method for SLE patients grounded in ML systems. Keywords: Systemic Lupus Erythematosus; medical treatment; machine learning; artificial intelligence 1. Introduction Systemic Lupus Erythematosus (SLE) is a chronic autoimmune affliction that affects various physiological systems. It serves as an exemplary autoimmune disorder, and its intricate nature poses significant challenges. The varied clinical presentations of SLE, coupled with distinct complexities in both diagnosis and treatment, present a formidable task for healthcare professionals. The emergence of multiple mechanisms results in the breakdown of self-tolerance and subsequent organ dysfunction. Progress in elucidating the molecular and cellular foundations of this condition, in conjunction with the identification of genetic variations, contributes to a more profound comprehension of its pathogenesis, offering promise for therapeutic advancements in the near future. Commonly known as lupus, it varies in prevalence depending on geographic location, ethnicity, and research study design. In the United States, an estimated 241 cases per 100,000 adults have been reported, while in Spain, the updated figure is 210 cases per 100,000 inhabitants [ 1 ]. The Lupus Foundation of America estimates that approximately 161,000 to 322,000 individuals in the U.S. are affected by SLE, translating to a prevalence Bioengineering 2024,11, 90. https://doi.org/10.3390/bioengineering11010090 https://www.mdpi.com/journal/bioengineering Bioengineering 2024,11, 90 2 of 16 of approximately 0.05% to 0.1% of the population. Predominantly, it affects young, fertile females and has resulted in increased mortality, although improved treatment modalities have positively impacted survival rates. Notably, the onset of the disease frequently occurs during the childbearing years. Certain demographic groups, including women, people of color (particularly African American, Hispanic, and Asian populations), and individuals of reproductive age, may experience higher prevalence rates. Simultaneously, several factors contribute to a state of relative immunodeficiency in individuals with SLE, including aging, the increasing use of targeted biologic therapies, and the chronic nature of the disease. Furthermore, the presence of other comorbidities such as malignancy, infections, malnutrition, and more further compounds the complexity of the disease. SLE is a complex and heterogeneous condition, manifesting symptoms across a spectrum from mild to severe. The precise etiology of SLE remains not fully understood, with its development believed to result from a combination of genetic and environmental factors. Moreover, the prevalence of SLE may undergo changes over time, influenced by factors such as improvements in diagnostic methods and increased awareness of the disease. Collectively, these multifaceted factors underscore the need for a comprehensive understanding of the diverse epidemiological and clinical aspects of SLE to inform effective management strategies and interventions. Emerging evidence suggests that immunodeficiency and systemic autoimmunity are interconnected manifestations of a shared underlying process [ 2 ]. Immune disorders present as both susceptibility to infections and autoimmune symptoms, indicating a dual impact on the immune system—reduced ability to clear infections and a disruption of selftolerance. On the other hand, infections are one of the most common causes of death and are often associated with high levels of activity in SLE. Early diagnosis of immunodeficency in SLE is the first step to contribute to detect infections, which are likely to be associated with flares, allows prompt initiation of treatment, a better prognosis, and a reduction in organ dysfunction [ 3 – 7 ]. In the absence of specific criteria that can differentiate between a severe infection and an exacerbation in SLE, the development of clinical studies and guidelines becomes imperative to facilitate a more precise classification of these patients [ 8 ]. In pursuit of this objective, Machine Learning (ML) draws inspiration from biological nervous systems. Its fundamental principle revolves around presenting algorithms with input data, subjecting them to computer analysis to predict output values within an acceptable range of accuracy, recognizing data patterns and trends, and ultimately assimilating knowledge from prior experiences [9]. ML delves into intricate data distributions, establishes probabilistic relationships, and identifies the minimum set of features required to capture essential data patterns through repeated cross-validation, culminating in the formulation of predictive models. Numerous studies have leveraged ML methods to develop more precise diagnostic algorithms for stratifying autoimmune diseases, thereby preventing or mitigating observed morbidity [ 10 ]. ML methods consistently exhibit superior performance compared to traditional statistical models [ 9 , 11 – 13 ]. A variety of ML techniques, including Support Vector Machine (SVM), Binary Linear Discriminant Analysis (BLDA), k-Nearest Neighbors (KNN), and Decision Trees (DT) [ 14 – 17 ], have been employed for data analysis. These systems represent a selection of algorithms designed for classifying data and processing information, and they have been explored in the context of various autoimmune diseases, including SLE, rheumatoid arthritis, lupus tubulointerstitial inflammation, and neuropsychiatric SLE [18–23]. In this paper, we present a system that utilizes the Random Forest (RF) method for the analysis of immunodeficiency patterns in SLE patients. RF is an ML algorithm that operates by constructing a multitude of decision trees for classification and prediction. For its capacity to enhance accuracy and processing speed, and several notable advantages, including a low computational burden, flexibility in model tuning, high scalability, and algorithmic optimization, it serves as the cornerstone of this approach. Through the application of RF, we aim to predict the immunodeficiency status of our patients, with Bioengineering 2024,11, 90 3 of 16 the overarching goal of not only identifying optimal treatment options but also designing personalized preventive measures and tailoring patient-specific follow-up strategies. The paper is structured as follows. The first section outlines the topic, purpose, and significance of this study. Second section introduces a detailed description of material and methods. Third section entails the main findings of the study, including data, analysis, and interpretation of the results obtained. Fourth section explores a discussion of these results. And finally, the paper concludes with a summary of the research and some concluding remarks. 2. Materials and Methods 2.1. Materials The study cohort included 125 patients who met the American College of Rheumatology criteria for SLE in 2019 [ 23 ]. These individuals were enrolled from the Autoimmune Unit Registry at Valladolid Clinic Hospital (HCUV) between 2017 and 2019. The experimental protocol adhered to the principles outlined in the Declaration of Helsinki (2008) and received approval from the Clinical Research Ethics Committee of the HCUV. The study was conducted in compliance with Spanish data protection laws (LO 15/1999) and specifications (RD 1720/2007). Consequently, a retrospective review of patients was systematically conducted, encompassing the collection of epidemiological, analytical, immunological, and clinical characteristics. Relevant immunological parameters for evaluating immune competence included leucocytes, neutrophils, CD3, CD4 and CD8 T-cell counts, CD19 B-cell and Natural Killer (NK) cell levels, serum immunoglobin isotypes (IgG, IgA, IgM), IgG subclasses, and complement levels (C3, C4). Exclusion criteria involved patients with evidence of active disease (SLEDAI >= 4) or significant residual proteinuria ( > 500 mg). Following this selection strategy, 31 patients were excluded from the study. Flow cytometry was performed to identify cell populations. Serum levels of immunoglobulin isotypes and IgG subclasses and complement were determined by nephelometry. Standardized reference ranges from the immunology laboratory of our institution were used to define control patients. Laboratory levels below the reference ranges were considered as possible immunodeficiency status: leucocytes < 4000 cL/ µ L, neutrophils <1800 cL/µL , lymphocytes < 1500 cL/ µ L, CD3 T-cell < 700 cL/ µ L, CD19 B-cell < 100 cL/ µ L, CD4 T-cell < 300 cL/ µ L, CD8 T-cell < 200 cL/ µ L, N K cell <90 cL/µL , IgG <870 mg/dL , IgG1 < 383 mg/dL, IgG2 < 242 mg/dL, IgG3 < 22 mg/dL, IgG4 < 4 mg/dL, IgA < 117 mg/dL, IgM < 60 mg/dL, C3 < 90 mg/dL, C4 < 10 mg/dL; special data for patients between 14 and 18 years old were: IgG1 < 315 mg/dL, Ig G2 <242 mg/d L, IgG3 < 23 mg/dL, IgG4 < 11 mg/dL. Severe infection was defined as infection which required hospitalization of seriousness, treatment needed or recommended monitoring. 2.2. Method This study introduces an ML method centered on the Random Forest (RF) algorithm. RF, a widely adopted ML algorithm within supervised learning, is applied for both classification and regression challenges in ML. Renowned for its simplicity, versatility, and robustness, RF embodies a potent ML algorithm with several noteworthy attributes: (1) operative as an ensemble learning approach, it combines decisions from multiple models to improve overall performance; (2) employing decision trees as base-level models; (3) mitigating overfitting by averaging results across several trees, thereby diminishing the risk of developing complex models performing well on training data but poorly on new data; (4) adeptly handling missing values by learning the optimal imputation value based on the reduction in the utilized criterion; (5) furnishing a reliable estimate of the importance of variables in the classification process; (6) demonstrating flexibility in its applicability to both regression and classification tasks; and (7) executing swiftly with minimal preprocessing requirements compared to alternative algorithms, capable of handling categorical variables Bioengineering 2024,11, 90 4 of 16 without necessitating the creation of dummy variables. Consequently, RF is the chosen algorithm for crafting the model aimed at detecting immunodeficiency patterns within the SLE population [24,25]. Given a dataset S={xj , yj} , where xj represents feature vectors and yj corresponds to labels, the RF algorithm proceeds as follows: For each of the ntrees in the forest: 1. Draw a bootstrap sample Z∗of size Nfrom the training data. 2. Grow a decision tree Tb to the bootstrapped data by recursively repeating the following steps for each terminal node of the tree, until the minimum node size nmin is reached: (a) Select mvariables at random from the pvariables. (b) Pick the best variable/split-point among the mvariables. (c) Split the node into two daughter nodes. The prediction of the RF then aggregates the predictions of the ntrees. For regression, it is typically the average over all trees: b fr f (x) = 1 n n ∑ b=1 Tb(x)(1) For classification, it is determined by the majority vote: b Cr f (x) = majority{b Cb(x)}n 1(2) Here, Tb(x) and b Cb(x) represent the prediction of the b -th decision tree for regression and classification, respectively. The algorithm was designed and developed using Matlab software (MatLab 2023a, The Mathworks Inc., Natick, MA, USA). Furthermore, the proposed system underwent analysis alongside other ML systems prevalent in the scientific community. These included Support Vector Machine (SVM) [ 14 ], Binary Linear Discriminant Analysis (BLDA) [ 26 ], Decision Trees (DT) [ 15 ], Linear Regression (LR) [ 27 , 28 ], and k-Nearest Neighbor (KNN) [ 16 ] to assess its performance. Within the ML system’s learning process, it is imperative to control overtraining. To address this, the k-fold cross-validation technique was employed in our case. As depicted in Figure 1, each iteration involves the random classification of 70% of the patients for training and 30% for testing and validation. Notably, patient data are not shared between the training and validation subsets to prevent the algorithm from being validated with data from the same patients used in the training phase. Figure 1. The figure shows the processes followed in this study for the classification of patients with SLE. Bioengineering 2024,11, 90 5 of 16 Additionally, techniques for hyperparameter optimization have been applied to finetune the hyperparameters of the methods. These hyperparameter values are adjusted during the training phase to maximize the accuracy of the ML method. The hyperparameters subjected to optimization encompass variables such as apprentices, neighbors, distance metric, distance weight, kernel, box constraint level, and multiclass method, each tailored to the specific requirements of the method in use. Bayesian optimization was chosen as the technique to enhance the performance of the various methods by optimizing the selection of diverse hyperparameters. Recall value and AUC were utilized as performance metrics. The entire study was iterated 100 times to obtain mean values and standard deviations for the process. Importantly, it should be emphasized that data used in each iteration were randomized, mitigating noise in the samples and ensuring the acquisition of results with statistically valid values [29]. 2.3. Performance Evaluation For this study, the most well-known metrics in artificial intelligence were implemented to test the performance of the methods [ 29 ]: balanced accuracy (BA), recall, precision, specificity (SP), degenerated Younden’s index (DYI) [ 29 ], receiver operating characteristic (ROC) and area under the curve (AUC). The F1score is established as: F1score =2Precision ·Recall Precision +Recall (3) To test the classification performance of the model, the Matthew correlation coefficient (MCC) has been used, which is described as follows: MCC =TP ·TN −FP ·FN p(TP +FP)(TP +FN)(TN +FP)(TN +FN)(4) where TP is the number of true positives, FP the number of false positives, TN the number of true negatives and FN the number of false negatives. And finally, Cohen’s Kappa (CK), CK is another metric that estimates the performance of the model [29]. 3. Results The study was conducted on a group of 125 patients diagnosed with SLE. Out of these, 94 patients met the specific criteria of having a SLEDAI-2K score of less than four points, and were thus included in the study. Further analysis revealed that 77 of these 94 patients showed signs of immunodeficiency. This means that approximately 81.9% of the patients with a SLEDAI-2K score less than four exhibited signs of immunodeficiency. The cohort of patients had a median age of 52 years, whilst the median age at diagnosis was 38 years. The group was predominantly female, with 68 female patients compared to 9 male patients. The median duration of the disease among these patients was 14 years. At the time of data collection (see Table 1), 50 patients (64.9%) were being treated with corticosteroids at an average daily dose of 2.57 mg. In addition, 25 patients (34.9%) were receiving immunosuppressants such as azathioprine, methotrexate, and mycophenolate. Two patients were on belimumab treatment. Notably, none of the patients were undergoing treatment with rituximab. In turn, 41 patients (53.2%) exhibited patterns of immunodeficiency. Among these patients, there were a total of 51 episodes of severe infections. The breakdown of these infections is as follows: • 17 patients were hospitalized due to lower respiratory infections. • 4 patients were hospitalized for upper respiratory infections. • 9 patients were treated for urinary infections. • 10 patients had soft tissue infections. • 4 patients suffered from digestive infections. • 1 patient was diagnosed with tuberculous lymphadenitis. Bioengineering 2024,11, 90 6 of 16 Table 1provides an overview of the characteristics of patients exhibiting immunodeficiency patterns. The patients under study demonstrated a decline in the count of several immune cells. This was particularly evident in the case of NK cells, a component of the innate immune system, and CD19 B-cells, a part of the adaptive immune system. The latter includes IgG subclasses and IgM, both of which also showed a decrease. These patients exhibited reduced levels of various immune cells, as illustrated in Table 1, with notable decreases observed in NK cells within the innate immune system and CD19 B-cells within the adaptive immune system, including IgG subclasses and IgM. Table 1. Characteristics of patients with immunodeficiency patterns. Characteristics of Patients with Immunodeficiency Patterns N 77 Median age (years) 52 Female/Male 68/9 SLE evolution time (years) 14 Corticosteroids (n) 50 (64.9%) Immunosuppressants (n) 25 (32.4%) Hydroxychloroquine (n) 37 (48%) Severe infections (n) 51 Immunodeficiency patterns (n) Leucocytes (<4000 cL/µL ) 9 Lymphocytes (<1500 cL/µL) 28 Neutrophils (<1800 cL/µL) 9 CD3 (<700 cL/µL) 10 CD4 (<300 cL/µL) 6 CD8 (<200 cL/µL) 3 CD19 (<100 cL/µL) 23 NK (<90 cL/µL) 13 IgG (<870 mg/dL) 17 IgG1 (<383 mg/dL) 3 IgG2 (<242 mg/dL) 36 IgG3 (<22 mg/dL) 16 IgG4 (<4 mg/dL) 7 IgA (117 mg/dL) 8 IgM (<60 mg/dL) 20 C3 (<90 mg/dL) 13 C4 (<10 mg/dL) 6 The study employed a range of ML techniques to discern patterns of innate and adaptive immunodeficiency within the SLE population. The findings derived from these techniques, coupled with several ML algorithms for identifying immunodeficiency, are detailed below. Performance metrics such as BA, recall, specificity, precision, and AUC for the investigated ML methods are exhibited in Tables 2and 3. Both tables provide a detailed summary of performance metrics for different ML methods applied to variables IgG, IgG2, IgG3, IgG4 (Table 2), and IgM, NK, CD19, CD3 (Table 3). These variables are associated with immunoglobulins and immune cell populations, whilst the ML methods evaluated include SVM, BLDA, DT, KNN, and the RF proposed method. The results offer insights into how well each ML method performs in predicting or classifying the specified immunological variables, providing a comparative analysis of their strengths in terms of these metrics. The comprehensive nature of the data facilitates an informed selection of the most suitable method for each variable based on the desired performance criteria. Of particular note is the RF proposed method, which consistently outperforms across all variables, achieving the highest accuracy. KNN also demonstrates strong performance, particularly in IgM and CD3. LR were the lowest results obtained, whilst SVM, BLDA, and DT generally exhibit competitive results but with slightly lower accuracy than RF and KNN. In summary, the evaluation underscores the robust performance of the proposed Bioengineering 2024,11, 90 7 of 16 method across the variables related to immunoglobulins and immune cell types, being the preferred model for classifying SLE patients due to its consistently high accuracy, balanced performance metrics, ensemble learning strengths, and robustness to noisy data observed. Table 2. The table summarises the values of BA, recall, specificity, precision and AUC for variables IgG, IgG2, IgG3 and IgG4. IgG. Methods BA Recall Specificity Precision AUC SVM 80.85 80.95 80.76 80.28 80.00 BLDA 78.11 78.20 78.02 77.55 78.00 DT 83.85 83.95 83.75 83.25 83.00 LR 70.02 69.75 68.84 68.95 68.42 RF 93.96 94.07 93.85 93.29 94.00 KNN 86.38 86.48 86.28 85.76 86.00 IgG2. Methods BA Recall Specificity Precision AUC SVM 81.85 81.95 81.76 81.27 81.00 BLDA 77.37 77.46 77.28 76.82 77.00 DT 83.16 83.26 83.06 82.57 83.00 LR 69.51 69.24 68.33 68.44 68.42 RF 94.58 94.69 94.47 93.90 94.00 KNN 85.99 86.09 85.89 85.38 86.00 IgG3. Methods BA Recall Specificity Precision AUC SVM 81.56 81.66 81.47 80.98 81.00 BLDA 79.16 79.25 79.06 78.59 79.00 DT 83.82 83.92 83.72 83.22 83.00 LR 69.44 69.17 68.27 68.38 68.42 RF 94.42 94.53 94.31 93.75 94.00 KNN 86.57 86.67 86.47 85.95 86.00 IgG4. Methods BA Recall Specificity Precision AUC SVM 81.35 81.45 81.26 80.77 81.00 BLDA 78.93 79.02 78.83 78.36 78.00 DT 83.26 83.36 83.16 82.67 83.00 LR 70.15 69.88 68.97 69.08 68.42 RF 94.50 94.61 94.39 93.83 94.00 KNN 86.07 86.17 85.97 85.46 86.00 Table 3. The table summarises the values of BA, recall, specificity, precision and AUC for variables IgM, NK, CD19 and CD3. IgM. Methods BA Recall Specificity Precision AUC SVM 81.24 81.34 81.15 80.67 81.00 BLDA 78.11 78.20 78.02 77.55 78.00 DT 83.35 83.45 83.25 82.76 83.00 LR 69.86 69.59 68.68 68.79 68.42 RF 94.80 94.91 94.69 94.12 94.00 KNN 86.38 86.48 86.28 85.76 86.00 Bioengineering 2024,11, 90 8 of 16 Table 3. Cont. NK. Methods BA Recall Specificity Precision AUC SVM 81.06 81.16 80.97 80.49 81.00 BLDA 77.52 77.61 77.43 76.97 77.00 DT 84.84 84.94 84.74 84.24 84.00 LR 69.51 69.24 68.33 68.44 68.42 RF 94.75 94.86 94.64 94.07 94.00 KNN 86.51 86.61 86.41 85.89 86.00 CD19. Methods BA Recall Specificity Precision AUC SVM 82.21 82.31 82.12 81.63 82.00 BLDA 76.89 76.98 76.80 76.34 76.00 DT 84.04 84.14 83.94 83.44 84.00 LR 69.65 69.38 68.47 68.58 68.42 RF 94.34 94.45 94.23 93.67 94.00 KNN 85.24 85.34 85.14 84.63 85.00 CD3. Methods BA Recall Specificity Precision AUC SVM 81.46 81.56 81.37 80.88 81.00 BLDA 77.21 77.30 77.12 76.66 77.00 DT 84.16 84.26 84.06 83.56 84.00 LR 70.41 70.14 69.22 69.33 68.42 RF 95.12 95.23 95.01 94.44 95.00 KNN 86.38 86.48 86.28 85.76 86.00 Moreover, Tables 4and 5present performance metrics, including F 1 score, MCC, DYI, and Kappa values, for the ML methods applied. The observed values provide insights into the models’ effectiveness in classifying SLE patients. Thus, in Table 4(variables IgG, IgG2, IgG3, and IgG4), RF consistently outperforms again other methods across all metrics, exhibiting high F 1 score, MCC, DYI, and Kappa values. This suggests RF’s robustness in achieving a balanced trade-off between precision and recall, capturing the model’s ability to handle both positive and negative instances effectively. Again, KNN also shows competitive performance, while SVM, BLDA, and DT demonstrate slightly lower performance across these metrics, being LR the one which obtained the lowest performance values. Similar trends are observed in the variables related to immune cell types in Table 5 (IgM, NK, CD19, and CD3), where RF again demonstrates superior performance, especially notable in achieving high F 1 score and DYI values. This reinforces RF’s suitability for SLE classification, indicating its ability to maintain a balance between true positives, true negatives, false positives, and false negatives. KNN also perform well, but RF consistently stands out as the top-performing model across the diverse set of variables. For a comprehensive view of the trade-off between the true/false positive rates between the proposed system and other ML methods, the Receiver Operating Characteristic (ROC) curves were also generated. With this purpose in mind, the ROC curve is employed to quantify sensitivity and 1-specificity at various threshold levels. As illustrated in Figure 2 , which shows the ROC curve for CD19 variable as example, the system that utilizes RF generates the largest area under the curve, indicating a superior level of predictive accuracy. Bioengineering 2024,11, 90 9 of 16 Table 4. The table presents the F 1 score, MCC, DYI and Kappa values for variables IgG, IgG2, IgG3 and IgG4. IgG. Methods F1score MCC DYI Kappa SVM 80.61 71.74 80.85 71.98 BLDA 77.87 69.31 78.11 69.54 DT 83.60 74.40 83.85 74.65 LR 70.06 64.59 69.83 64.23 RF 93.68 83.37 93.96 83.65 KNN 86.12 76.65 86.38 76.90 IgG2. Methods F1score MCC DYI Kappa SVM 81.61 72.63 81.85 72.87 BLDA 77.14 68.65 77.37 68.88 DT 82.91 73.79 83.16 74.04 LR 69.54 64.12 69.32 63.76 RF 94.30 83.92 94.58 84.20 KNN 85.73 76.30 85.99 76.55 IgG3. Methods F1score MCC DYI Kappa SVM 81.32 72.37 81.56 72.61 BLDA 78.92 70.24 79.16 70.47 DT 83.57 74.38 83.82 74.62 LR 69.48 64.06 69.25 63.70 RF 94.14 83.78 94.42 84.06 KNN 86.31 76.81 86.57 77.07 IgG4. Methods F1score MCC DYI Kappa SVM 81.11 72.19 81.35 72.43 BLDA 78.69 70.03 78.93 70.27 DT 83.01 73.88 83.26 74.13 LR 70.19 64.72 69.96 64.35 RF 94.22 83.85 94.50 84.13 KNN 85.81 76.37 86.07 76.62 Table 5. The table presents the F 1 score, MCC, DYI and Kappa values for variables IgM, NK, CD19 and CD3. IgM. Methods F1score MCC DYI Kappa SVM 81.00 72.09 81.24 72.33 BLDA 77.87 69.31 78.11 69.54 DT 83.10 73.96 83.35 74.21 LR 69.90 64.45 69.67 64.08 RF 94.51 84.12 94.80 84.40 KNN 86.12 76.65 86.38 76.90 NK. Methods F1score MCC DYI Kappa SVM 80.82 71.93 81.06 72.17 BLDA 77.29 68.78 77.52 69.01 DT 84.59 75.28 84.84 75.53 LR 69.54 64.12 69.32 63.76 Bioengineering 2024,11, 90 16 of 16 41. 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