Comparative Analysis of Machine Learning Models for Rheumatoid Arthritis Prediction Using Multiclass Clinical Data
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Comparative Analysis of Machine Learning Models for Rheumatoid ArthritisPrediction using Multiclass Clinical Data
Santosh Kalshetty
Department of Computer Engineering Siddant
Engineering College Pune, India
santoshkalshetty159@gmail.com
Prof. Nanda S. Kulkarni
Head of Department Siddant Engineering College Pune,
India
Abstract—Rheumatoid Arthritis (RA) is a chronic autoimmune inflammatory disorder that leads to joint destruction and long-term disability if not diagnosed early. Machine learning tech- niques enable efficient analysis of multidimensional clinical data for early prediction. This study presents a comparative evalu-tionofLogistic Regression, Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN) for multiclass arthritis classification. Performance is evaluated using accuracy, precision, recall, F1-score, confusion matrix, AUC-ROC curves, SHAP explainability, and statistical validation. Experimental resultsdemonstrate that Random Forest achieves superior overall performance.
Index Terms—Rheumatoid Arthritis, Multiclass Classification, Machine Learning, Random Forest, SHAP, ROC Curve
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