Medi-Assist: A Decision Tree based Chronic Diseases Detection Model

Abstract

Rise of predictive techniques have changed the manner we deal with disease diagnosis in healthcare domain. Earlier detail analysis of patient data records was done for disease prediction to develop complicated prototypes to detect trends. In recent times, disease symptoms assessement has been more accurate and effective with the rise of data analytics based intelligence. These technologies enable the development of predictive models that can analyze large volumes of patient data and identify hidden correlations, allowing for the early detection and diagnosis of diseases. This research paper explores the development of a Multiple Disease Prediction System Using Machine Learning (ML) that predicts three major diseases: heart disease, diabetes, and Breast Cancer disease. To predict diseases, the system incorporates machine learning models such as Decision Tree & support vector machine. The model is designed to be user friendly and very easy to use so that any beginner can use it easily. It has an intuitive web interface built using Python, Django and Streamlit API, allowing users to input their health data and get disease predictions. The system's effectiveness in predicting the three chronic diseases has been evaluated and compared with other traditional models to show its superior performance. Accuracy using decision tree algorithm for breast cancer, heart disease & diabetes prediction was found to be 97.98%, 92.62%, and 91.55% respectively.

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