Diagnosis of diabetes using classification mining techniques

TL;DR

This paper explores using classification mining techniques, specifically Decision Tree and Naive Bayes algorithms, to improve the speed and accuracy of diabetes diagnosis based on documented health data.

Contribution

It introduces a classification-based approach employing Decision Tree and Naive Bayes algorithms for more efficient diabetes diagnosis.

Findings

Decision Tree achieved 85% accuracy.

Naive Bayes achieved 80% accuracy.

Proposed methods can potentially lead to quicker diagnosis.

Abstract

Diabetes has affected over 246 million people worldwide with a majority of them being women. According to the WHO report, by 2025 this number is expected to rise to over 380 million. The disease has been named the fifth deadliest disease in the United States with no imminent cure in sight. With the rise of information technology and its continued advent into the medical and healthcare sector, the cases of diabetes as well as their symptoms are well documented. This paper aims at finding solutions to diagnose the disease by analyzing the patterns found in the data through classification analysis by employing Decision Tree and Na\"ive Bayes algorithms. The research hopes to propose a quicker and more efficient technique of diagnosing the disease, leading to timely treatment of the patients.