Machine Learning–based Multi-Disease Prediction

Abstract

This paper proposes a machine learning–based multi-disease prediction system that integrates disease-specific classifiers within a unified, real-time clinical decision-support platform. The framework employs Support Vector Machine (RBF) for diabetes prediction, Support Vector Machine (linear) for heart disease, Decision Tree for chronic kidney disease (CKD), and Logistic Regression for cancer prediction, with each classifier selected according to the statistical characteristics of its respective dataset. The system is implemented using a Streamlit-based web interface, enabling efficient real-time prediction with interpretable outputs. Experimental evaluation demonstrates strong predictive performance, achieving accuracies ranging from 85.71% to 94.30% and AUC-ROC values between 0.91 and 0.97 across the four disease modules, representing a 7.2 percentage-point improvement over comparable unified prediction systems reported in the literature. The modular architecture provides scalability, low computational complexity, and rapid inference, making it suitable for pre-diagnostic screening in clinical environments. The proposed framework offers an effective and practical solution for early chronic disease detection while supporting future expansion. Planned enhancements include the integration of deep learning models for medical imaging and electronic health records, Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME, wearable and IoT-based continuous health monitoring, federated learning for privacy-preserving distributed model training, and prospective clinical validation through hospital information system integration. These developments are expected to improve prediction accuracy, interpretability, scalability, and clinical applicability for next-generation intelligent healthcare systems.