> ## 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.

# Introduction

> Binary classification with deep learning on the UCI Heart Disease dataset.

# Heart Disease Classification

This project builds and evaluates a binary classification neural network to predict the presence of heart disease in patients. Using the Heart Disease UCI dataset, it systematically explores different architectural choices — activation functions, optimizers, learning rates, and batch sizes — to understand their real-world impact on model performance.

> Academic context: Mid-term project for a Deep Learning course, guided by Doç. Dr. Öğr. Üyesi Abdullatif KABAN.

## What This Project Covers

<CardGroup cols={2}>
  <Card title="Dataset" icon="database" href="/docs/dataset">
    1,025 patient records with 13 clinical features from the UCI Heart Disease dataset.
  </Card>

  <Card title="Architecture" icon="network-wired" href="/docs/architecture">
    A 3-hidden-layer neural network designed for tabular binary classification.
  </Card>

  <Card title="Experiments" icon="flask" href="/docs/experiments/activation-functions">
    Systematic comparison of activation functions, optimizers, learning rates, and batch sizes.
  </Card>

  <Card title="Results" icon="chart-line" href="/docs/results">
    Best configuration achieves \~97% test accuracy with strong precision and recall.
  </Card>
</CardGroup>

## Tech Stack

| Tool                 | Role                    |
| -------------------- | ----------------------- |
| Python 3.10+         | Language                |
| TensorFlow / Keras   | Model training          |
| scikit-learn         | Preprocessing           |
| pandas / NumPy       | Data handling           |
| Matplotlib / Seaborn | Visualization           |
| Jupyter Notebook     | Development environment |