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A Web-based multi-disease Prediction framework using Machine learning approaches

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Author Affiliations

  • 1St Pauls College, Bangalore, Karnataka, India
  • 2GFGC and PG centre Shankaranarayana, Udupi, Karnataka, India

Res. J. Recent Sci., Volume 15, Issue (3), Pages 86-90, July,2 (2026)

Abstract

The increasing reliance on data-driven technologies in healthcare has created new opportunities for developing intelligent systems capable of supporting early disease identification. Rather than relying solely on conventional diagnostic procedures, machine learning techniques enable the analysis of complex medical datasets to uncover latent patterns associated with disease progression. In this work, a web-based multi-disease prediction framework is developed to estimate the likelihood of diabetes, heart disease, and Parkinson’s disease using supervised machine learning algorithms. The system is implemented in Python and deployed via the Streamlit framework to ensure accessibility and ease of use. Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest classifiers are trained and evaluated using standard medical datasets. Model performance is assessed using accuracy, precision, recall, and F1-score metrics. Experimental observations indicate that ensemble-based approaches provide more consistent and robust predictions across datasets. The developed framework demonstrates the practical feasibility of integrating machine learning models into lightweight web applications for preliminary disease risk assessment and clinical decision support.

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