An Effective Feature Selection for Type II Diabetes Prediction

Diabetes is a condition related to metabolism that arises due to various reasons. Each year, a significant portion of patients experience life-threatening complications leading to fatality. This illness is classified into three distinct forms: Type 1, Type 2, and Gestational diabetes. It is of utmost importance to predict type 2 diabetes, which arises from cellular insulin utilization deficiency or secretion disorder, as it enables the prevention of complications or delays in the onset of the disease. Predicting the occurrence of illnesses using machine learning and artificial intelligence can substantially mitigate its costs. However, due to interpretability challenges in proposed models, physicians and patients are hesitant to adopt them. Previous studies have utilized various algorithms including Naïve Bayes, SVM, KNN, and decision tree algorithms for patients’ classification. In this paper, conducted on the Pima dataset, we employed a preprocessing method utilizing the most significant features selected by the Random Forest algorithm. Additionally, for model testing, we utilized the SVM algorithm, known for its high discriminative power in binary classification tasks and its relatively good interpretability. The results indicate that the proposed model achieved an accuracy of 80.09%, outperforming other models by a 2.26% improvement. Furthermore, there were notable improvements in precision and specificity metrics with the proposed model. By utilizing these methods, web-based applications can be employed to motivate physicians and patients for diabetes prediction.