Machine Learning For Predicting Multiple Diseases

Abstract

Accurate and timely illness prediction has been made possible by machine learning techniques, which have completely changed the healthcare industry. Simultaneous prediction of numerous illnesses can greatly enhance early detection and treatment, improving patient outcomes and lowering healthcare expenditures. This study examines the use of machine learning algorithms to forecast a variety of illnesses, emphasising the advantages, difficulties, and potential applications. We give a summary of the several machine learning models and information sources that are often employed in illness prediction. We also go over the significance of feature selection, model assessment, and combining several data modalities for improved illness prediction. The study's conclusions demonstrate machine learning's potential for multi-disease prediction as well as its possible effects on public health. Once more, I'm using a machine learning model to determine whether or not an individual has a few diseases. This training model trains itself to predict illness using sample data.