Exploring Machine Learning Algorithms for Predicting Multiple Diseases

In the realm of healthcare, early detection and diagnosis of diseases hold paramount importance in saving lives and mitigating the risks associated with various disorders. Traditional machine learning models often focus on detecting individual diseases, overlooking the potential for a unified system capable of forecasting multiple ailments. By presenting a novel approach to building a unified system that can forecast multiple diseases with a single user interface, this study closes this gap. This research employs various machine learning techniques, such as Random Forest, Support Vector Machine (SVM), Deep Learning utilizing TensorFlow and Keras, as well as Logistic Regression. The proposed system aims to achieve high accuracy in disease prediction by using customized datasets for conditions like diabetes, kidney disease, liver disease, Parkinson’s disease, and heart disease. Using Streamlit Cloud and the Streamlit library, the interface is implemented as a web application and offers options for intuitive disease prediction.