Multi-Disease Prediction System using Machine Learning

Chronic diseases are depicted as conditions that require proceeding with medical consideration, limit everyday exercises, or both. Chronic diseases, for example, heart disease, pneumonia, kidney disease and diabetes are the main sources of death and disability on the planet. It is a critical challenge to detect these diseases by regular clinical data analysis. Early prediction of these chronic diseases would help in saving multiple lives. Machine learning models bring effective solutions for accurate predictions and decision making. Machine learning techniques have shown huge development in the medical industry. The paper attempts to do the predictive analysis of chronic diseases using machine learning. Chronic Kidney Disease will be predicted using Logistic Regression and Random Forest, Diabetes can be predicted using Logistic Regression (LR) and K-Nearest Neighbour (KNN), Heart Disease can be predicted using Random Forest Regression and Decision Tree and finally for Pneumonia can be predicted using a Convolutional Neural Network (CNN) on chest x-rays. An integrated model of the listed algorithms is proposed to predict the listed chronic diseases.