Integrating LSTM and LOF: A Comprehensive Approach to Anomaly Detection in Healthcare Data

Abstract

The Internet of Medical Things (IoMT) has grown substantially, facilitating extensive time series data accumulation within healthcare environments.Detecting anomalies in IoMT time series data holds significant importance for recognizing potential health hazards and upholding patient well-being. This studyinvestigates the efficacy of merging Long Short-Term Memory (LSTM) neural networks with the Local Outlier Factor (LOF) algorithm for anomaly detection intime series data. LSTM networks are adept at grasping extended dependencies in sequential data, while LOF represents a potent unsupervised outlier identification technique. We introduce a unique methodology that capitalizes on the strengths of both LSTM and LOF to heighten anomaly detection accuracy. The proposed technique undergoes assessment via experiments conducted on actual IoMT datasets sourced from the WUSTL-EHMS. Through comprehensive experimentation and analysis, we showcase its effectiveness. The results of the proposed method reveal a promising ability to precisely pinpoint anomalies, offering a valuable resource for healthcare practitioners in promptly identifying irregular patient conditions.