An infectious disease/fever screening radar system which stratifies higher-risk patients within ten seconds using a neural network and the fuzzy grouping method

OBJECTIVES: To classify higher-risk influenza patients within 10 s, we developed an infectious disease and fever screening radar system. METHODS: The system screens infected patients based on vital signs, i.e., respiration rate measured by a radar, heart rate by a finger-tip photo-reflector, and facial temperature by a thermography. The system segregates subjects into higher-risk influenza (HR-I) group, lower-risk influenza (LR-I) group, and non-influenza (Non-I) group using a neural network and fuzzy clustering method (FCM). We conducted influenza screening for 35 seasonal influenza patients and 48 normal control subjects at the Japan Self-Defense Force Central Hospital. Pulse oximetry oxygen saturation (SpO(2)) was measured as a reference. RESULTS: The system classified 17 subjects into HR-I group, 26 into LR-I group, and 40 into Non-I group. Ten out of the 17 HR-I subjects indicated SpO(2) <96%, whereas only two out of the 26 LR-I subjects showed SpO(2) <96%. The chi-squared test revealed a significant difference in the ratio of subjects showed SpO(2) <96% between HR-I and LR-I group (p < 0.001). There were zero and nine normal control subjects in HR-I and LR-I groups, respectively, and there was one influenza patient in Non-I group. CONCLUSIONS: The combination of neural network and FCM achieved efficient detection of higher-risk influenza patients who indicated SpO(2) 96% within 10 s.