Prediction of Diabetes using Supervised Learning Approach - Khozouie , Nasim; Rahmani Seryasat, Omid; Moshrefzadeh, Sadegh (Health Nexus, 2024)
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.