Multiple Disease Prediction System: A Review

Abstract

Machine Learning techniques are used for a lot of applications. In healthcare, machine learning plays a critical role in disease prediction. For detecting a disease, several tests must be required from the patient. By using machine learning techniques, the number of tests can be reduced. This reduced test performs a critical function in time and performance. This article analyzes machine learning strategies that can be used to predict distinct varieties of diseases. This paper reviewed the research papers which especially deal with predicting Diabetes, Heart disease, and Breast cancer. This article presents a review of various models based on such algorithms, techniques, and an analysis of their performance. Research has been carried out on various models of supervised learning algorithms and some of them are Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree, Naïve Bayes, Convolutional Neural Network and Random Forest (RF).