Machine learning implementation to predict type-2 diabetes mellitus based on lifestyle behaviour pattern using HBA1C status

Abstract

Purpose: According to the literature of International Diabetes Federation reports in 2019, one of the causes of adulthood death is diabetes. Diabetes is a chronic metabolic disease that has a long-term impact on the individual’s well-being. Insulin resistance and abnormal glucose metabolism are hallmarks of type 2 diabetes mellitus (T2DM). Noncommunicable Diseases (NCD), such as diabetes, are caused by poor eating habits and way of life. Methods: In this work, several risk prediction models for Type 2 Diabetes Mellitus have been proposed. Using multivariate analysis to assist patients with risk stratification within a population, this study also attempts to determine the relationship between lifestyle behavior patterns and diabetes. Considering that a number of diabetes predictive models proposed by previous researchers rely primarily on specific medical measurement data, but lack external diabetic factors, external diabetic factors are necessary for the development of diabetes predictive models. Glycated Hemoglobin (HbA1c) is utilized in the form to diagnose diabetes due to its efficiency and patient-friendliness. In addition, contrary to the widespread belief that machine learning is superior in many ways, a number of issues, such as racial bias, must be considered when deploying machine learning in the healthcare industry. Because HbA1c is influenced by external factors such as race and ethnicity, the Asia-Pacific region has a variety of HbA1c cut-off points. As a result, restricting the population scope must be viewed as the best approach in this task to permit improved accuracy and assurance. Results: A detailed experimental analysis of various machine learning model performances was evaluated on a standard Type-2 Diabetes dataset. Random forest with SMOTE oversampling and PCA technique outperformed the other methods for Type-2 Diabetes prediction with a recall of 65%, precision of 89%, and f1-score of 75%. The machine learning models performances were evaluated on various test cases during testing. Conclusions: This study leveraging a dataset of medical report and lifestyle behavior factor to predict the HbA1c category. The proposed approach can be deployed as a tool at medical centers to serve as an early Type-2 disease diagnosis tool and in addition, the tool can assist the medical experts in accurately predicting the Type-2 diabetes disease.