• Akash Mandal
• Shahebaj Pathan
• Mayur Kusmade
• Omkar Deshmukh
• Arya Sonawane

In the modern healthcare landscape, early detection and proactive disease management have become critical in addressing the rising burden of chronic illnesses. Diseases like Diabetes, Heart Disease, and Parkinson’s Disease are among the most common and life-altering conditions globally, yet they often go undiagnosed until significant symptoms emerge. The delay in diagnosis can lead to severe complications, reduced treatment effectiveness, and increased healthcare costs. To tackle this problem, there is a pressing need for intelligent, accessible tools that can assist individuals in assessing their health risks before critical symptoms manifest. This paper presents PredictiX, a machine learning-based system designed to predict the likelihood of multiple diseases based on user-provided medical data. The system utilizes widely available datasets—such as the Pima Indians Diabetes Dataset, UCI Heart Disease Dataset, and UCI Parkinson’s Dataset—to train and evaluate several supervised machine learning algorithms. These include Logistic Regression, Support Vector Classifier (SVC), Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Each algorithm is evaluated for optimal performance using metrics such as accuracy, precision, recall, and F1-score. PredictiX is deployed as a user-friendly web application built using Python and Streamlit, allowing users to input relevant health parameters and receive real-time predictions. The core objective of PredictiX is to empower users with early awareness of their health status and encourage timely medical consultations, acting as a supportive tool that helps bridge the gap between preventive care and clinical diagnosis..

Machine Learning, Artificial Intelligence, Healthcare, Disease Prediction, Diabetes, Heart Disease, Parkinson's Disease, Supervised Learning, Web Application.

"PredictiX: A Practical Framework for Multi-Disease Prediction using Supervised Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 9, page no.d553-d562, September-2025, Available :http://www.jetir.org/papers/JETIR2509375.pdf

"PredictiX: A Practical Framework for Multi-Disease Prediction using Supervised Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 9, page no. ppd553-d562, September-2025, Available at : http://www.jetir.org/papers/JETIR2509375.pdf