Human body is guarded by the immune system, but sometimes this immune system alone is not capable of preventing our body from diseases. Environmental conditions and living habits of people are the cause of many diseases that are the main reason for a huge number of deaths in the world, and diagnosing these diseases sometimes becomes challenging. We need an accurate, feasible, reliable, and robust system to diagnose diseases in time so that these can be properly treated. With the growth of medical data, many researchers are using these medical data and some machine learning algorithms to help the healthcare communities in the diagnosis of many diseases. In this paper a survey of various models based on such algorithms, techniques is presented and their performance is analyzed. Researches have been conducted on various models of supervised learning algorithms and some of them are Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), Naïve Bayes and Random Forest (RF).