Multiple Disease Detection System Using Biomarkers
DOI:
https://doi.org/10.32628/CSEIT251112272Keywords:
Biomarkers, Disease Prediction, Machine Learning, Non-Invasive Monitoring, Edge ComputingAbstract
The development of a real-time health monitoring system using biomarkers and Machine Learning (ML) algorithms, implemented on an IoT-enabled embedded platform for continuous disease prediction and preventive healthcare. The system integrates non-invasive sensors to track key biomarkers such as Temperature, Humidity, BP_Diastolic, SpO2, BPM and pH, enabling real-time monitoring of physiological parameters. Utilizing the computational capabilities of an edge-processing microcontroller, the collected data is processed using a K-Nearest Neighbor (KNN) algorithm, classifying health conditions based on biomarker variations for early disease detection. By optimizing ML models for low-power embedded devices, the implementation ensures a balance between accuracy and computational efficiency. The system provides a web-based dashboard for real-time health visualization, facilitating point-of-care applications and demonstrating the feasibility of deploying advanced predictive analytics on resource-constrained platforms for proactive healthcare management.
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