Integrating LSTM Autoencoders in Medical Monitoring Systems for Early Diabetes Detection

A study offers the exploration of LSTM Autoencoder use in anomaly detection applied to the prediction of diabetes from patient medical histories. The model has dual purposes through the utilization of reconstruction error in identifying diabetes cases and in correcting patient patterns to normal levels. Unsupervised training of the LSTM Autoencoder allows it to identify temporal relationships within sequential data that support medical record anomaly detection. The model outperformed traditional classifiers Random Forest and XGBoost when detecting anomalies through its substantial measurements of 92% accuracy, 0.86 F1 score, and 0.93 ROC- AUC. The demonstrated exceptional ability of the model to detect anomalies showed results of 92% accuracy together with metrics of 0.86 F1 score and 0.93 ROC-AUC. The autoencoder detects most anomalous cases according to the confusion matrix while producing minimal false negative results. Detecting sequential minor changes in time-series data by the model demonstrates its capability to identify diabetes at an early stage along with other chronic diseases. The obtained results showcase LSTM Autoencoders as a promising solution to boost predictive healthcare systems because they deliver an effective system for health anomaly detection and prompt medical treatment.