Advanced International Journal for Research

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Multi-Disease Risk Analytics System

Author(s) Ms. Nanthini S, Mr. Sudharsan J, Mr. Lokesh S, Mr. Bupeash S, Mr. Mohamed Fahim N
Country India
Abstract The rapid increase in chronic and infectious diseases worldwide has created an urgent demand for intelligent, accessible, and scalable health screening systems. This paper presents the Multi-Disease Risk Analytics System (MDRAS), a web-based artificial intelligence (AI) platform built to simultaneously predict risk for 15 diseases using supervised machine learning (ML) algorithms. The system employs Random Forest, Gradient Boosting, Support Vector Machine (SVM), and Logistic Regression classifiers, automatically selecting the best-performing model for each disease. Developed using Python 3.8 and the Streamlit framework, the platform features role-based access control (RBAC) for Patient and Administrator workflows, an AI-powered health chatbot for guided symptom-based screening, an analytics dashboard for prediction history, automated PDF report generation, and a professional dark-themed user interface. Diseases covered include Diabetes, Heart Disease, Kidney Disease, Liver Disease, Breast Cancer, Lung Cancer, Stroke, Parkinson's Disease, Thyroid Disease, Anemia, Pneumonia, Tuberculosis, Alzheimer's Disease, COVID-19, and Melanoma. Experimental results demonstrate model accuracies ranging from 75% to 99% across all 15 classifiers. MDRAS addresses the critical gap in existing single-disease prediction tools by offering a unified, secure, and user-friendly multi-disease screening platform.
Keywords Multi-Disease Prediction, Machine Learning, Healthcare AI, Streamlit, Random Forest, Gradient Boosting, SVM, Role-Based Access Control, Python, SQLite, Scikit-learn
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-01
DOI https://doi.org/10.63363/aijfr.2026.v07i03.5313
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