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Model Architecture

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Network Design

The neural network was designed to balance capacity and generalization for a 13-feature tabular dataset.
LayerNeuronsActivation
Input13—
Hidden 164ReLU
Hidden 232ReLU
Hidden 316ReLU
Output1Sigmoid
  • Loss function: Binary Cross-Entropy
  • Optimizer: Adam
  • Regularization: Dropout after each hidden layer

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Why 3 Hidden Layers?

The choice is grounded in the bias-variance trade-off:
  • Too few layers → underfitting (high bias), the model cannot capture meaningful patterns.
  • Too many layers → overfitting (high variance), the model memorizes training data.
Three layers sit at a reasonable middle ground for a dataset of 1,025 samples with 13 features. Alternative architectures (1 hidden layer and 5 hidden layers) were also tested to validate this choice.

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Why Sigmoid on the Output?

The output layer uses a single neuron with a Sigmoid activation, which squashes the output to the range [0, 1]. This directly represents the probability of heart disease being present, making it natural for binary classification with Binary Cross-Entropy loss.

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Regularization with Dropout

With only 1,025 training samples, overfitting is a real risk. Dropout randomly deactivates a fraction of neurons during each training step, preventing the network from co-adapting and forcing it to learn more robust representations.