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 Corresponding Author https://orcid.org/0000-0001-9765-565X
Omid Rahmani Seryasat Assistant Professor, Department of Electrical Engineering, Faculty of Technology and Engineering, Shams Higher Education Institute, Gorgan, Iran Author https://orcid.org/0000-0002-9289-6128
Sadegh Moshrefzadeh Department of Computer Engineering, Faculty of Technology and Engineering, Yasouj branch, Islamic Azad University, Yasouj, Iran Author https://orcid.org/0000-0002-2697-0334

DOI:

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.