Diabetic - 11.7z

Helping hospitals prioritize screenings for patients whose "Diabetic 11" profiles show rapid metabolic decline. 5. Proposed Visualization

Identify which clinical variables (e.g., HbA1c levels, BMI, blood pressure) are the strongest predictors of long-term complications within the 11-point data structure. Diabetic 11.7z

Analyze how patient health degrades or improves over the 11 recorded phases. Gradient Boosting (XGBoost)

Utilizing k-fold cross-validation specifically designed for longitudinal healthcare data to prevent data leakage. 4. Potential Findings & Impact Diabetic 11.7z

Compare Random Forests, Gradient Boosting (XGBoost), and LSTM networks for classification accuracy. 3. Methodology