• Dr. R Amutha
• Karthik M C
• Rohit M Sank
• BinduShree Y V
• Govardhan H

Machine learning and artificial intelligence have become integral components of modern society, permeating various sectors such as self-driving vehicles and healthcare. The medical field, in particular, accumulates vast amounts of patient data, providing fertile ground for diverse analytical approaches. Leveraging machine learning, we've developed a Prediction System capable of detecting multiple diseases simultaneously. This project represents a significant advancement over single-disease prediction systems known for their low accuracy. Ensuring accurate predictions is crucial, as accuracy if it is low then one can pose serious risks to patients' well-being. Our system utilizes machine learning algorithms such as Random Forest Classifier and Support Vector Machine (SVM). These models are deployed using Streamlit Cloud and the Streamlit library, offering a user-friendly interface for disease prediction. The application interface offers options for three diseases: diabetes, heart disease, and Parkinson's disease. Upon selecting a disease, users are prompted to input relevant parameters for the prediction model. This initiative addresses the critical need for precise disease prediction, facilitating early detection and intervention. Furthermore, after predicting the disease, the system incorporates a pill reminder feature to alert patients about their medication schedule, enhancing adherence and overall health management.

Machine Learning, Random Forest Classifier, Support Vector Machine (SVM), Streamlit, Diabetes, Heart, Parkinsons.

"MULTIPLE DISEASE PREDICTION SYSTEM USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.201-209, May-2024, Available :http://www.jetir.org/papers/JETIRGG06032.pdf

"MULTIPLE DISEASE PREDICTION SYSTEM USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pp201-209, May-2024, Available at : http://www.jetir.org/papers/JETIRGG06032.pdf