Analysis and Prediction of Diabetes Using Machine Learning

Abstract

Healthcare industry contains very large and sensitive data and needs to be handled very carefully. Diabetes Mellitus is one of the growing extremely fatal diseases all over the world. Medical professionals want a reliable prediction system to diagnose Diabetes. Different machine learning techniques are useful for examining the data from diverse perspectives and synopsizing it into valuable information. The accessibility and availability of huge amounts of data will be able to provide us useful knowledge if certain data mining techniques are applied to it. The main goal is to determine new patterns and then to interpret these patterns to deliver significant and useful information for the users. Diabetes contributes to heart disease, kidney disease, nerve damage, and blindness. Mining the diabetes data in an efficient way is a crucial concern. The data mining techniques and methods will be discovered to find the appropriate approaches and techniques for efficient classification of Diabetes dataset and in extracting valuable patterns. In this study, medical bioinformatics analyses have been accomplished to predict diabetes. The WEKA software was employed as a mining tool for diagnosing diabetes. The Pima Indian diabetes database was acquired from UCI repository used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses diabetes disease. In this study, we aim to apply the bootstrapping resampling technique to enhance the accuracy and then applying Naïve Bayes, Decision Trees and (KNN) and compare their performance.

Keywords: Healthcare, Diabetes, Classification, K-nearest neighbors, Decision Trees, Naive Bayes