Published Paper Details:

The primary objective of our project is to develop a comprehensive health monitoring system capable of detecting the presence of critical medical conditions such as Heart Disease, Diabetes, and Parkinson's Disease. Users can input relevant health parameters, and the system employs machine learning algorithms to analyze the data, providing real-time feedback on potential health risks. This project utilizes a userfriendly interface that allows individuals to input their health information easily. The system then processes this data, leveraging machine learning and statistical models to assess the likelihood of heart disease, diabetes, and Parkinson's disease. The results are promptly communicated to the user, enabling them to take proactive measures for their well-being. The machine learning algorithms taken into consideration for detecting Heart Disease is Logistic Regression, Diabetes is Random Forest and Parkinson's Disease is Support Vector Machine. Once the data is taken from the users for the selected disease detector, the values will be used by the classifiers to detect if the individual has the particular disease or not. This system is implemented using Python programming and Streamlit library for integrating the models to front end.