Diabetes Prediction using Stacking (Stacked Generalization)

• The architecture of a stacking model involves two or more base models, often referred to as level-0 models, and a meta-model that combines the predictions of the base models, referred to as a level-1 model.

• We have used the following models as level-0 models:

1. Gaussian Naive Bayes Classifier (With hyperparameter tuning)
2. Random Forest Classifier (With hyperparameter tuning & 3-fold CV)
3. Decision Tree Classifier (With hyperparameter tuning & 3-fold CV)
4. SVM Classifier (With hyperparameter tuning & 3-fold CV)
5. ANN Model (With hyperparameter tuning & 3-fold CV)
6. Logisitic Regression (With hyperparameter tuning & 3-fold CV)

• We have used simple "Logistic Regression" Model (from python's 'scikit' module) as the level-1 model, with 4-fold CV.

• We have achieved 82.68% test accuracy on "Our Model", which is better than all the 6 individual models.

Contributors & Authors

Vinay Khilwani, Vasu Gondaliya, Shreya Patel, Jay Hemnani & Bhuvan Gandhi