> ## Documentation Index
> Fetch the complete documentation index at: https://heart.metesahankurt.cloud/llms.txt
> Use this file to discover all available pages before exploring further.

# Model Architecture

> A 3-hidden-layer neural network for binary classification on tabular data.

# Model Architecture

## Network Design

The neural network was designed to balance capacity and generalization for a 13-feature tabular dataset.

| Layer    | Neurons | Activation |
| -------- | ------- | ---------- |
| Input    | 13      | —          |
| Hidden 1 | 64      | ReLU       |
| Hidden 2 | 32      | ReLU       |
| Hidden 3 | 16      | ReLU       |
| Output   | 1       | Sigmoid    |

* Loss function: Binary Cross-Entropy
* Optimizer: Adam
* Regularization: Dropout after each hidden layer

## 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.

## 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.

## 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.