Machine learning classification approaches for prediction of effective diabetes drugs

Abstract

Diabetes, a complex and widespread metabolic disease, presents unique challenges for individuals and healthcare systems alike. This paper describes a model for personalized diabetes treatment by employing various classification approaches to assist medical professionals in accurately prescribing medications to patients. The primary objective was to predict the most appropriate drug treatment for individual patients by applying multi-label and multi- target classification techniques, we developed classification models that can improve the health of diabetic patients including predicting the risk of readmission for each patient by using two main approaches, the first approach is multi-label classification, this approach aimed to predict the most suitable drug treatment class for the patient. The second approach applied was multi-target classification, this approach will predict the most suitable drug treatment and the patient’s readmission. By considering multiple factors and characteristics specific to each patient, the model determined the suitable drug treatment based on their features and condition. To achieve a high-quality prediction of the suitable drug for diabetic patients, we employed feature engineering to enhance the efficiency and effectiveness of the machine learning algorithms used in the personalized treatment methodology. The experimental results indicate that the classification approaches are highly accurate when used to predict appropriate drug treatment for diabetes patients. The Naïve Bayes classifier reached an average accuracy of 98.72 %. Using cost-sensitive algorithms raised the average accuracy to 98 %.