prediction of diabetes using supervised learning approach

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.