Attempt to predict heart stroke by using models from sklearn and using keras

In this case we have dataset with 5110 patient records, but only 5% of them diagnosed as a heart attack.

The best results we achived using sofisticated method to replicate data (SMOTE) for minority class and neural network, built with:

• 2 dense layers with 1024 and 256 units,
• dropout layer (rate 0.1) and batchnormalization after each dense layer,
• dense layer with 2 units and softmax activation layer as an output,
• binary_crossentropy loss function,
• Adam Optimizer with learning rate = 0.0001

Our model results is:

88% accuracy in general, 93% precision for class 0 (No heart attack) and 83% for class 1, 81% recall for class 0 and 94% of class 1

Our results tell us that final model can detect 83% of heart attacks, classifing correctly each patient with symphtoms as a potential heart attack with 94% probability (recall). Kaggle competition worksheet link