Decision Support System for predicting Diseases using A.I.

AI in Healthcare

This study presents a novel medical decision support system leveraging Artificial Intelligence to diagnose Diabetes, Heart Disease, and Parkinson's disease through a unified platform. Our approach integrates three distinct machine learning models, each specifically trained on individual datasets for the respective conditions. The system's frontend, developed using Streamlit, features dedicated pages for diabetes, heart disease, and Parkinson's, enabling users to input relevant bloodwork details and receive immediate diagnostic feedback. This streamlined navigation allows medical professionals to efficiently assess multiple conditions from a single interface. The system is particularly beneficial for rural areas with limited access to specialized medical practitioners, where a single healthcare worker, such as a nurse, can use the tool to provide early diagnoses and prevent major health issues. Additionally, the system is cost-effective by reducing the need for specialized equipment and personnel, time-efficient by providing rapid diagnostic results, and supports remote monitoring, allowing patients to receive care without traveling to healthcare facilities. Its interoperability with other healthcare systems ensures seamless data flow and comprehensive patient management. Key findings demonstrate the models' high accuracy and robustness, potentially improving patient outcomes and healthcare delivery in underserved regions.

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