Anomaly Detection in Healthcare: A Deep Learning Approach with Autoencoders

This work investigates the use of autoencoders in deep learning for anomaly detection in the healthcare domain, with a focus on ethical and interpretable issues. The study creates a strong anomaly detection system by utilizing secondary data, a deductive methodology, and an interpretivist philosophy. The results demonstrate a high degree of judgment ability, recall, and precision. By improving interpretability, feature importance analysis solves the deep learning models' “black-box” problem. Responsibly using data is prioritized by confidentiality techniques and ethical considerations. Investigating hybrid models and evaluating adaptability to various healthcare scenarios are among the recommendations. Subsequent research will focus on integrating multi-modal data, enhancing the model with arising architectures, and resolving deployment issues.