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Corresponding Author ORCID Identifer

Umi Najiah Ahmad Razimi https://orcid.org/0000-0002-1567-4011
Yong Yoke Leng https://orcid.org/0000-0001-8976-5787

Section

(Thematic) AI in Health and Wellness

Abstract

Chronic Non-Communicable Diseases (NCDs), such as diabetes mellitus, hypertension, and cardiovascular disease, make up a significant portion of the mortality cases in Malaysia, as well as impose consistent demands for costs related to health care expenditure. According to the cost reports at the national level, major NCDs (cardiovascular disease, diabetes, and cancer) are responsible for RM 9.65 billion annually as direct healthcare cost [3]. However, the current approach to managing chronic diseases focuses heavily on periodic assessments in clinic setting at weekly or monthly intervals. Here, a predictive analysis framework based on AI technology, leveraging continuous monitoring with patient data collected from consumer wearables to predict potential health events up to five days ahead is proposed. A synthetic dataset was generated to simulate the behaviour of 500 patients affected by diabetes and/or hypertension, who were continuously monitored throughout a year for heart rate, heart rate variability, physical activity, sleep indices, and blood pressure features. The models are compared to determine the optimal algorithm in the case study. The LSTM model showed 93.7% accuracy in predicting five days in advance, outperforming the random forest model (91.5%), XGBoost model (90.6%), and the simple threshold rule-based approach (78.6%). The sensitivity and specificity scores of the LSTM model were 87.6% and 95.9%, respectively, while the ROC-AUC score was 0.983. According to the SHAP analysis, previous heart rate values (history for three days and seven days) have the largest contributions in predicting patient outcomes, followed by changes in heart rate variability and low activity levels. Although evaluation is based on synthetic data for methodological validation, the framework is intended for extension to real-world wearable streams to support earlier risk detection and more proactive chronic disease management.

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