AN ANALAYSIS OF DISEASE PREDICTION ALGORITHMS

The aim of using machine learning algorithms for disease prediction is to immensely help to solve health-related problems by assisting physicians in predicting and diagnosing diseases in an early phase. The Disease Prediction methodology is based on predictive modelling. This predicts the patient's disease based on the symptoms they provide as input. In this method, it analyses the patient's symptoms and returns the disease's probability as an output. The accuracy of machine learning models for disease prediction depends on a number of factors, including the quality of the training data, the choice of algorithm, and the amount of computing power available. The algorithms then identify the patterns that are associated with each disease. This allows them to predict the likelihood of a patient having a particular disease, with their symptoms and other clinical data. As technology continues to develop, it is likely to play an increasingly important role in the diagnosis and treatment of diseases. With better prediction algorithms, the medical practitioners can improve their ability to respond to emergency situations and render better quality treatment and save lives. These also help in rendering a quality treatment on an affordable cost with a well-developed predictive model. This article focuses on various disease prediction algorithms at present and analyses various metrics and their pros and cons.