Key result
A hierarchical health decision support system integrating wearable medical sensors achieved diagnostic accuracies of 86% for arrhythmia, 78% for type-2 diabetes, and up to 99% for other conditions.
A novel hierarchical health decision support system integrating wearable medical sensors with machine learning demonstrates high diagnostic accuracy across multiple disease categories.
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High accuracies suggest wearable diagnostic potential in arrhythmia; hypothesis-generating and requires prospective validation before clinical adoption.
Yin et al. (2017) studied Various diseases (arrhythmia, type-2 diabetes, urinary bladder disorder, renal pelvis nephritis, hypothyroid). Hierarchical health decision support system integrating wearable medical sensors was evaluated on Diagnostic accuracy. A hierarchical health decision support system integrating wearable medical sensors achieved diagnostic accuracies of 86% for arrhythmia, 78% for type-2 diabetes, and up to 99% for other conditions.
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