A Survey on ML Algorithms for Disease Prediction Used in Cutting-Edge Medical Care Administration Systems

In humans, one of the main causes of death is disease. Early detection of illness allows doctors to carry out appropriate treatment which can increase the chance of survival of a patient. Existing diagnosis is mostly based on manual treatment which needs a labor force in large numbers to diagnose every patient which is not possible for the large population. But in some last years, scientists have tried to design a Computer-Aided Design (CAD) system which is suitable for executing machine learning algorithms that help doctors detect various diseases. This paper reviews different algorithms including SVM, Logistic Regression, Decision Tree, and Random Forest that are mostly used to predict multiple diseases. We also provide the results of other ML Algorithms for predicting diseases. For conducting this review, we focused on two diseases which are Diabetes and Heart Disease. In this paper, we do a comparison between multiple techniques or algorithms used for predicting diabetes and heart disease based on their accuracy scores. This survey paper provides knowledge of various prediction techniques used for the prediction of both diseases. This paper summarizes all the information regarding algorithms and their performance, which can help in creating a better model in the future.