Mining Healthcare Data for Predictive Modeling and Prognosis of Alzheimer's Disease Progression with Artificial Intelligence

In this study, we propose a novel approach to predict and prognosticate Alzheimer's disease progression by leveraging cutting-edge artificial intelligence and data mining techniques. Our method integrates three powerful algorithms: Long Short-Term Memory Networks (LSTM), Random Forest, and Gradient Boosting Machines (GBM). LSTM excels in sequence analysis, effectively capturing temporal patterns in healthcare data crucial for disease progression prediction. Random Forest is utilized for feature importance analysis and classification, aiding in understanding the most influential aspects of healthcare data. GBM, known for ensemble learning, combines weak learners iteratively to create a robust predictive model, improving overall accuracy. By synergistically integrating these algorithms, our method optimizes predictive modeling and prognosis for Alzheimer's disease progression, ultimately enhancing patient care and management.