Machine Learning Implementation and Challenges: A Study of Lifestyle Behaviors Pattern and Hba1c Status

Abstract

Diabetes is a chronic metabolic disease that has a long-term impact on the individual’s well-being and one of the causes of adulthood death. This research paper represents an attempt to find the correlation between lifestyle behavior patterns and diabetes by leveraging machine learning in the form to facilitate patients with risk stratification in a population. The major findings that emerged were as follows: an unhealthy lifestyle and dietary pattern lead to Noncommunicable Disease (NCD) including diabetes. In the form to identify diabetes, Glycated Hemoglobin (HbA1c) will be used to diagnose diabetes considering its efficiency and convenience to the patient. Furthermore, contrary to what has been assumed of the superiority of machine learning has been provided in many aspects, few challenges should be taken into consideration when it comes to the implementation of Machine Learning in the healthcare field, racial bias, for instance. In the Asia Pacific region, there is a range of cut-off point of HbA1c values due to HbA1c is subject to external factors such as race and ethnicity. Therefore, narrowing down the population scope in healthcare is considered in this paper as the best practice to facilitate better accuracy and assurance.

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