Streamlit-Powered Comprehensive Health Analysis and Disease Prediction System

Maintaining well-being is vital, and healthcare services play a pivotal role in society. Unlike existing AI models focusing on individual disease prediction, our goal is to create a unified platform using the Streamlit Python module, predicting multiple diseases. Employing machine learning algorithms like random forest, Logistic Regression, SVM classifiers, and Extra Tree Classifier, we identify the algorithm with the highest accuracy during the dataset training phase. This approach allows for the examination of conditions such as diabetes, heart disease, and Parkinson's disease, considering parameters like pulse rate, cholesterol levels, blood pressure, and heart rate. The project's scope extends to encompassing additional chronic illnesses and skin conditions. By leveraging core health indicators, this study demonstrates the potential to predict a broad spectrum of diseases. The significance lies in early detection and intervention, contributing to a reduction in mortality rates.