Comparison of Machine Learning Algorithms for Prediction of Diabetes

Machine Learning method is increasingly used for data analysis, where large databases has to be analyzed. One of the areas where solutions include the use of machine learning is the field of medical prediction, used to observe the probability of a person may suffer from a disease in the future. One of the domains of medicine prediction in which machine learning solutions are used is predictions in the case of diabetes. Diabetes is a disease that is increasingly present in today's society. This paper presents a comparison among the results experimentally obtained, using three machine learning algorithms in the prediction of diabetes. The three considered algorithms are support vector machine, Naive Bayes, and random forest. The aim of this paper is to analyze the performance of the algorithms considering different metrics in order to compare different techniques to obtain better accuracy. We found that support vector machine and random forest obtained an accuracy of over 80%.