  Diabetes is one of the chronic diseases, which is increasing from year to
year. The problems begin when diabetes is not detected at an early phase and
diagnosed properly at the appropriate time. Different machine learning
techniques, as well as ontology-based ML techniques, have recently played an
important role in medical science by developing an automated system that can
detect diabetes patients. This paper provides a comparative study and review of
the most popular machine learning techniques and ontology-based Machine
Learning classification. Various types of classification algorithms were
considered namely: SVM, KNN, ANN, Naive Bayes, Logistic regression, and
Decision Tree. The results are evaluated based on performance metrics like
Recall, Accuracy, Precision, and F-Measure that are derived from the confusion
matrix. The experimental results showed that the best accuracy goes for
ontology classifiers and SVM.
