Prediction of Diabetes using Supervised Learning Approach

Nasim  Khozouie; Omid  Rahmani Seryasat; Sadegh  Moshrefzadeh

doi:10.61838/kman.hn.2.2.12

Authors

Nasim Khozouie * Assistant Professor, Department of Computer Engineering, Faculty of Technology and Engineering, Yasouj University, Yasouj, Iran n.khozouie@yu.ac.ir

Omid Rahmani Seryasat Assistant Professor, Department of Electrical Engineering, Faculty of Technology and Engineering, Shams Higher Education Institute, Gorgan, Iran

Sadegh Moshrefzadeh Department of Computer Engineering, Faculty of Technology and Engineering, Yasouj branch, Islamic Azad University, Yasouj, Iran

https://doi.org/10.61838/kman.hn.2.2.12

Keywords:

diabetes prediction, diagnosis, data mining, algorithms

Abstract

This paper provides an in-depth evaluation of various supervised machine learning models used for predicting diabetes. It discusses the strengths and limitations of several algorithms, including Decision Trees, Random Forest, Rotation Forest, Ensemble Classifier, K-Star, Simple Bayes, Logistic Regression, Functional Tree, and Perceptron Neural Network. The study utilizes a publicly available diabetes dataset from chistio.ir, which includes 520 samples, comprising 200 diabetic patients and 320 non-diabetic patients, and assesses 16 features. Results are validated on the Weka 3.6 open-source platform, using metrics such as AUC, classification accuracy (CA), F1 score, precision, and recall.