Diabetes Diagnosis through Machine Learning: An Analysis of Classification Algorithms

Abstract

Diabetes is a serious and chronic disease characterized by high levels of sugar in the blood. If left untreated, it can
lead to numerous complications. In the past, diagnosing diabetes required a visit to a diagnostic center and
consultation with a doctor. However, the use of machine learning can help to identify the disease earlier and more
accurately. This study aimed to create a model that can accurately predict the likelihood of diabetes in patients using
three machine learning classification algorithms: Logistic Regression (LR), Decision Tree (DT), and Naive Bayes
(NB). The model was tested on the Pima Indians Diabetes Database (PIDD) from the UCI machine learning
repository and the performance of the algorithms was evaluated using various metrics such as accuracy, precision,
F-measure, and recall. The results showed that Logistic Regression had the highest accuracy at 71.39%
outperforming the other algorithms.