A Comparative Analysis of Machine Learning Algorithms for Classification of Diabetes Utilizing Confusion Matrix Analysis

Abstract

Healthcare experts have been employing machine learning more and more in recent years to enhance patient outcomes and reduce costs. In addition, machine learning has been applied in various areas, including disease diagnosis, patient risk classification, customized treatment suggestions, and drug development. Machine learning algorithms can scrutinize vast quantities of data from electronic health records, medical images, and other sources to identify patterns and make predictions, which can support healthcare professionals and experts in making better-informed decisions, enhancing patient care, and determining a patient's health status. In this regard, the author opted to compare the performance of three algorithms (logistic regression, Adaboost, and naïve bayes) through the correct classification rate for diabetes prediction in order to ensure the effectiveness of accurate diagnosis. The dataset applied in this work is obtained from the Vanderbilt university institutional repository and is publicly available data. The study determined that three algorithms are very effective at prediction. Mainly, logistic regression and Adaboost had a classification rate above 92%, and the naive bayes algorithm achieved a classification rate above 90%.