Compare Random Forests, Gradient Boosting (XGBoost), and LSTM networks for classification accuracy. 3. Methodology
Utilizing k-fold cross-validation specifically designed for longitudinal healthcare data to prevent data leakage. 4. Potential Findings & Impact
Helping hospitals prioritize screenings for patients whose "Diabetic 11" profiles show rapid metabolic decline. 5. Proposed Visualization
Creating "delta" features that represent the change in health markers between the 11 recorded points.
Analyze how patient health degrades or improves over the 11 recorded phases.
Below is a proposal for a high-impact paper using this data:
