Abstract
Previous non-invasive Diabetes Mellitus (DM) prediction methods for rapid screening suffered from the trade-off between speed and accuracy. The accurate results of questionnaires rely on long and detailed questions thus sacrifice speed, meanwhile, photoplethysmography (PPG) offers convenient and fast testing but lacking accuracy. In this work, we developed a 5-grade model to accurately screen out non-DM subjects (low prediction grades) via one-minute PPG measurement. This efficient and effective rapid screening will practically reduce the loading for further invasive verification on the remaining DM-grade subjects. A total of 2538 subjects are recruited (DM: 1310, non-DM: 1228) with two 1-minute PPG samples taken from each subject. The model includes 8 features: 3 autonomic- and 3 vascular-related PPG features, heart rate, and waist circumference. All 8 features monotonically alter with increased DM prediction grade. The model provides users 5 DM risk grades. While defined grade 1 and grade 2 as non-DM grades, the prediction result shows a low false-negative rate of 13%. If only considering grade 1 as non-DM, the false-negative rate will be significantly reduced to 1.3%. Thus subjects predicted as grades 1 and 2 are substantially away from DM. The remaining subjects with higher DM risk grades such as grades 3, 4, and 5 (or unlikely grade 2) are recommended to take clinical-standard invasive DM test for corresponding therapeutic treatment. A table for assessing the risk index for each feature is also compiled. We have experimentally demonstrated a 1-minute pulsation measurement with PPG-based device (SpO <sub>2</sub> oximeter, smartphone, or wearable device) can be an efficient/effective DM rapid screening technique to filter out non-DM subjects. The resulted high-risk feature indexes also pose as warning signs of the degradation of either autonomic or vascular functions for personal healthcare management. The fast and convenient execution and useful results suggest that our approach is very simple and informative for quick DM risk assessment.
Highlights
Diabetes mellitus (DM) population is growing rapidly and often accompanied with cardiovascular diseases (CVD), which increases the mortality rate [1]
This study demonstrates a probability-based 5-grade classifications scenario for DM risk prediction
Conclude that this technique with waist circumference and PPG signal-derived features is capable of rapidly screening out non-DM subjects
Summary
Diabetes mellitus (DM) population is growing rapidly and often accompanied with cardiovascular diseases (CVD), which increases the mortality rate [1]. To detect or monitor disease progression on the rapidly increasing DM population, an efficient DM detection method with some information on disease progression is in need. An accurate diagnosis of diabetes remains expensive and inconvenient due to the time-consuming and invasive processes, such as traditional oral glucose tolerance test [2], HbA1C test [3], and C-peptide test [4]. Current non-invasive DM classification methods can be generally divided into questionnaire-based and signal-based binary classification models. They avoid uncomfortable or painful invasive methods, there are still various issues that prevent them from widely used in clinical settings.
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