Early-Stage Chronic Disease Prediction Using Deep Learning: A Comparative Study of LSTM and Traditional Machine Learning Models
Early-stage chronic disease prediction is a critical aspect of healthcare that allows for timely interventions and personalized treatment, ultimately improving patient outcomes. In this study, we explore the use of deep learning techniques, particularly Long Short-Term Memory (LSTM) networks, to predict the early stages of chronic diseases such as diabetes, cardiovascular diseases, and respiratory conditions. We compare the performance of LSTM with traditional machine learning models, including Random Forest, Gradient Boosting Machines (GBM), and Logistic Regression. The results show that LSTM outperforms the other models in terms of accuracy, precision, recall, F1-score, and AUC, demonstrating its superior ability to capture complex, temporal dependencies in medical data. The study highlights the potential of deep learning for early disease detection and its implications for personalized medicine, telemedicine, and healthcare optimization. However, challenges related to data quality, interpretability, and model generalization across diverse populations remain, and future work should address these issues to enhance the real-world applicability of AI-driven healthcare solutions.
