Review on Diabetes Detection Using Machine Learning Approaches

At present healthcare is one of the biggest concerns in the world. diabetes is the leading cause of death worldwide. Prediction and detection of the occurrences of a diabetes at the early stages is a valuable work in the medical field. There are several factors which are responsible for the diabetes such as Blood glucose level, insulin resistance, insulin production, MI (Body mass index) family background etc. In healthcare, There has been a significant amount of study. on the prediction of heart diseases, Brian stroke, but relatively little attention is paid on predicting a diabetes. The primary goal of this research is to review various research publications earlier and to choose the best machine learning techniques for the prediction of diabetes for our future work. After reviewing the different machine learning methods utilized for diabetes predictions and after taking into account the previously published studies, it has been discovered that death rate and functional results are the expected outcomes for the majority of the research work done. Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, Neural Networks and Naive Bayes were the most frequently employed methods.