Abstract
Type 2 diabetes mellitus (T2DM) often results in high morbidity and mortality. In addition, T2DM presents a substantial financial burden for individuals and their families, health systems, and societies. According to studies and reports, globally, the incidence and prevalence of T2DM are increasing rapidly. Several models have been built to predict T2DM onset in the future or detect undiagnosed T2DM in patients. Additional to the performance of such models, their interpretability is crucial for health experts, especially in personalized clinical prediction models. Data collected over 42 months from health check-up examinations and prescribed drugs data repositories of four primary healthcare providers were used in this study. We propose a framework consisting of LogicRegression based feature extraction and Least Absolute Shrinkage and Selection operator based prediction modeling for undiagnosed T2DM prediction. Performance of the models was measured using Area under the ROC curve (AUC) with corresponding confidence intervals. Results show that using LogicRegression based feature extraction resulted in simpler models, which are easier for healthcare experts to interpret, especially in cases with many binary features. Models developed using the proposed framework resulted in an AUC of 0.818 (95% Confidence Interval (CI): 0.812−0.823) that was comparable to more complex models (i.e., models with a larger number of features), where all features were included in prediction model development with the AUC of 0.816 (95% CI: 0.810−0.822). However, the difference in the number of used features was significant. This study proposes a framework for building interpretable models in healthcare that can contribute to higher trust in prediction models from healthcare experts.
Highlights
Morbidity and mortality are often results of Type 2 diabetes mellitus (T2DM)
(3535 cases left), (c) cases not having a previous diabetes diagnosis (3176 cases left) and entries where: (d) fasting plasma glucose level (FPGL) was not reported giving us a total of 3120 records of patient visits were left for development of a prediction model to estimate the risk of undiagnosed T2DM
We present selected logic attributes extracted from the dataset for the undiagnosed T2DM prediction use case
Summary
Morbidity and mortality are often results of Type 2 diabetes mellitus (T2DM). In addition, T2DM presents a substantial financial drain for individuals and families, health systems, and societies. The incidence and prevalence of T2DM are increasing rapidly [1]. In 2017, it was estimated that 425 million people had any diabetes 5.5% of the worldwide population), of which 90% had T2DM. The prevalence is going to increase substantially in the coming years; by 2045, for example, a 48% increase of prevalence from the above numbers is expected, or in absolute numbers, an estimated 629 million people 6.6% of the worldwide population) are expected to be suffering from any diabetes [2]. T2DM can lead to a substantially increased risk of macrovascular and microvascular disease, especially in inadequate glycemic control [3]. Impaired fasting glucose typically leads to slow progression of T2DM and, more importantly, its symptoms may remain undetected for many years
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