Machine Learning Techniques for Anomaly Detection in Smart Healthcare

Anomaly detection is a vital research problem among the different domains intrusion detection, fraud detection, device health monitoring, fault data detection, event detection in sensor networks. Anomalies mean an outlier, noise, novelties, exceptions which do not match the expected behavior of the system. Machine learning techniques work well in identifying these abnormal patterns. In this paper, the unsupervised clustering technique K-means, and its variation K-medoids partitioning are applied to detect anomalies. Sensor-embedded wearable devices are allowing smart healthcare services for people even in remote areas. These devices support continuous monitoring of people's health and allow the caregivers to provide better health assistance. Early-stage anomaly detection in such types of smart healthcare practices increases the efficiency of health services. In experimental discussion, K-means, and K-medoids partitioning clustering algorithms are assessed, and their performance is addressed.