Abstract
Diabetes is a costly and burdensome metabolic disorder that occurs due to the elevation of glucose levels in the bloodstream. If it goes unchecked for an extended period, it can lead to the damage of different body organs and develop life-threatening health complications. Studies show that the progression of diabetes can be stopped or delayed, provided a person follows a healthy lifestyle and takes proper medication. Prevention of diabetes or the delayed onset of diabetes is crucial, and it can be achieved if there exists a screening process that identifies individuals who are at risk of developing diabetes in the future. Although machine learning techniques have been applied for disease diagnosis, there is little work done on long term prediction of disease, type 2 diabetes in particular. Moreover, finding discriminative features or risk-factors responsible for the future development of diabetes plays a significant role. In this study, we propose two novel feature extraction approaches for finding the best risk-factors, followed by applying a machine learning pipeline for the long term prediction of type 2 diabetes. The proposed methods have been evaluated using data from a longitudinal clinical study, known as the San Antonio Heart Study. Our proposed model managed to achieve 95.94% accuracy in predicting whether a person will develop type 2 diabetes within the next 7–8 years or not.
Highlights
Diabetes mellitus (DM) is a chronic metabolic disorder requiring continuous glycemic control for associated risk reduction
According to the World Health Organization (WHO), diabetes is diagnosed if fasting plasma glucose (PG0) value is ≥126 mg/dL or two-hour plasma glucose (PG120) is ≥200 mg/dL after 75g of oral glucose intake [2]
In an attempt to further the limited research done in the prediction of type 2 diabetes mellitus (T2DM), we propose a novel approach that a) incorporates two new feature extraction schemes, b) selects features/risk-factors that are highly correlated with the future development of T2DM, and c) implements a machine learning model to predict the future progression of T2DM
Summary
Diabetes mellitus (DM) is a chronic metabolic disorder requiring continuous glycemic control for associated risk reduction. A hormone generated in the pancreas gland of the body, carries glucose from the bloodstream into the body cells [1]. The lack of insulin leads to the rise of blood glucose levels and progresses the development of diabetes. According to the World Health Organization (WHO), diabetes is diagnosed if fasting plasma glucose (PG0) value is ≥126 mg/dL or two-hour plasma glucose (PG120) is ≥200 mg/dL after 75g of oral glucose intake [2]. The consequence of diabetes affects national health-care budgets, slows down economic growth, and increases healthcare expenditure [3].
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