• Pranav Sahu
• Lalita Panika
• Tushar Pandey
• Kunal Sahu
• Arya Sharma

The integration of machine learning (ML) into healthcare has revolutionized disease diagnosis and prognosis. This paper introduces Web-Based Multi-Disease Diagnosis System Using Machine Learning , a robust ML-based system designed to predict seven critical diseases: diabetes, heart disease, chronic kidney disease, breast cancer, lung cancer, liver disease, and Parkinson’s disease. Utilizing diverse datasets from Kaggle, this model employs a suite of algorithms including Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), LightGBM, Extra Trees Classifier, and ensemble methods. The system demonstrates high predictive accuracy across all diseases, showcasing its potential as a scalable tool for early diagnosis and clinical decision support.

Machine learning, Healthcare, Diseases, Diagnosis .

"Web-Based Multi-Disease Diagnosis System Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.b215-b221, May-2025, Available :http://www.jetir.org/papers/JETIR2505124.pdf

"Web-Based Multi-Disease Diagnosis System Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppb215-b221, May-2025, Available at : http://www.jetir.org/papers/JETIR2505124.pdf