Sequential Data Analysis in Healthcare: Predicting Disease Progression with Long Short-Term Memory Networks

This study uses secondary data to forecast the course of disease using Long Short-Term Memory (LSTM) networks in an interpretive framework. By means of a descriptive design, temporal patterns are explored using a deductive approach. Metrics such as accuracy, precision, recall, alongside area under the
$\text{ROC}$
curve are used to illustrate the predictive accuracy of the LSTM model in the results. The temporal pattern analysis highlights the LSTM's ability to identify subtle trends by revealing the dynamic evolution of diseases over time. Analysis of feature importance sheds light on the temporal and clinical elements affecting predictions. The critical analysis identifies areas for improvement and highlights methodological strengths. Suggestions include sensitivity analyses, explained disclosure of hyper parameter tuning, and validation of data representativeness. For reliable, scalable applications in health care, future work must integrate various data modalities, provide real-time updating mechanisms, and take care of model explainability issues.