Diabetes Prediction using Machine learning and Data Mining Methods

Abstract Diabetes mellitus, commonly known as diabetes, is a metabolic disease. It is an extremely regular disease to the humankind from young to oldster. A persistent disease appears when blood glucose level is too high. Hence, to reduce the increasing rate of diabetes, diagnosing diabetes is very important. Data Analytics is a methodical procedure of examining and recognizing the concealed pattern from huge measure of information to reach conclusions. In medical science, this methodical procedure is implemented by using different machine learning algorithms to analyze the medical data like K-Nearest Neighbors, Support Vector Classifier, Logistic Regression, Gaussian Naive Bayes, and Random Forest. The objective of this research is to utilize significant features rather than using all the features. Therefore, we performed the data cleaning along with the potential feature selection and then used the Logistic Regression. Proposed approach outperform with some existing approaches that are using the machine learning algorithms.