Comparative Analysis of Machine Learning Models for Rheumatoid Arthritis Prediction

Kulkarni, Dr. Rahul; Salunkhe, Sujata; Kalshetty, Santosh

doi:https://doi.org/10.55041/ijcope.v2i5.282

Volume 02, Issue 05

Published on: May 2026

COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR RHEUMATOID ARTHRITIS PREDICTION

Dr. Rahul Kulkarni Sujata Salunkhe Santosh Kalshetty

Prof. Nanda S. Kulkarni

Dept. of Computer Engineering

Siddant Engineering College Pune

DOI:https://doi.org/10.55041/ijcope.v2i5.282

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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- ation of Logistic 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 results demonstrate that Random Forest achieves superior overall performance.

Index Terms—Rheumatoid Arthritis, Multiclass Classification, Machine Learning, Random Forest, SHAP, ROC Curve

How to Cite this Paper

Kulkarni, R., Salunkhe, S. & Kalshetty, S. (2026). Comparative Analysis of Machine Learning Models for Rheumatoid Arthritis Prediction. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(05). https://doi.org/10.55041/ijcope.v2i5.282

Kulkarni, Rahul, et al.. "Comparative Analysis of Machine Learning Models for Rheumatoid Arthritis Prediction." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i5.282.

Kulkarni, Rahul,Sujata Salunkhe, and Santosh Kalshetty. "Comparative Analysis of Machine Learning Models for Rheumatoid Arthritis Prediction." International Journal of Creative and Open Research in Engineering and Management 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i5.282.

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•Published on: May 09 2026

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