Smart System for Predicting Lung Health Using the K-Nearest Neighbors Algorithm on the Internet of Medical Things Platform
DOI:
https://doi.org/10.33795/jartel.v15i1.6371Keywords:
Health, Lung, Spirometer, Internet of Medical Things, K-Nearest NeighbourAbstract
The lungs play a vital role in oxygen regulation and overall respiratory stability, yet they are highly susceptible to both infectious and non-infectious diseases. Respiratory illnesses such as COPD, pneumonia, lung cancer, tuberculosis, asthma, and hypoxia remain major global health problems and leading causes of mortality. Early detection of lung health conditions is therefore essential to prevent severe complications. Indicators such as changes in nail color, including bluish discoloration, may signal respiratory disorders. Although spirometry is commonly used to assess lung function, its high cost and reliance on clinical procedures limit routine monitoring. This study proposes an intelligent lung health prediction system based on the Internet of Medical Things (IoMT) using the K-Nearest Neighbors (K-NN) algorithm. The system integrates a TCS3200 sensor for nail RGB color detection, a MAX30100 sensor for pulse rate and oxygen saturation (SpO₂), and an MPX5500DP sensor to measure Forced Vital Capacity (FVC) and Forced Expiratory Volume in one second (FEV1). Experimental results show sensor errors of 2.1% (BPM), 0.51% (SpO₂), 5.07% (FVC), and 8.2% (FEV1). The system achieved 97.07% accuracy using K-NN (k = 11), enabling real-time lung health monitoring via smartphone and supporting early respiratory disease detection.
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