Designing Smart Healthcare Wearables for Remote Patient Monitoring Using Decision Tree-Based Anomaly Detection Algorithms

Remote health care monitoring has in recent past boosted the emergence of smart wearables for real time health data. This research aims at developing and implementing novel healthcare wearables for generating the decision tree-based anomaly detection algorithms for remote patient monitoring. These wearables are expected to periodically monitor vital parameters including heartbeat, blood pressure, temperature and oxygen levels and relay that information to healthcare givers for monitoring. The decision tree based algorithms work a great deal in detecting discrepancies and signals of possible health deviations from the values of data received. It manages to make sure timely medical intervention when it is required, hence cutting down on hospitalizations and enhancing patient's lives' quality. The research also extend to applying machine learning models such as decision trees for processing large amount of data that is collected from wearables and fine tunable an models to achieve better results. From the design outlined here, it is believed that this design will provide a robust, flexible, low-cost solution to improving healthcare in the developing world. Employing concepts such as patient safety, health checkup from time to time, the wearable system acts as a preventive check to patients' health and as such is vital. It also describes major issues that wearable device has to solve: battery issues, security, and compliance, thus giving future guidelines for the growth of smart healthcare.