Heart Failure Analysis

July 8, 2026

Heart Failure Analysis Project

A data-driven project that uses machine learning and AI to analyze heart failure data. The goal is to build models that reveal patterns in the dataset and help explain which factors contribute most to heart failure risk.

What this project does

  • Uses supervised learning to train predictive models on labeled health data.
  • Applies neural networks to capture complex relationships in the features.
  • Includes data preprocessing, training, validation, and visualization steps.
  • Saves the model weights and biases to .npy files for later reuse.

Key concepts

1. Supervised Learning

The project trains models on labeled heart failure data, letting the model learn how input features relate to the outcome.

2. Neural Networks

Neural networks are used to model non-linear patterns and interactions between medical features.

3. Data Preprocessing

Important preprocessing steps include:

  • handling missing values
  • normalizing numeric features
  • splitting the dataset into training and validation sets

4. Model Evaluation

Model performance is measured with standard metrics like:

  • accuracy
  • precision
  • recall
  • F1-score

5. Ethical AI

The project emphasizes responsible AI by:

  • respecting data privacy
  • avoiding biased predictions
  • noting that this is not a medical diagnosis tool

Project structure

PyCharmMiscProject/
├── analiza_insuficiena_cardiaca.ipynb  # Jupyter Notebook for analysis
├── heart.csv                          # Dataset
├── model/                             # Directory for model weights
│   ├── best_train_b.npy
│   ├── best_train_w_in.npy
│   ├── best_valid_b.npy
│   ├── best_valid_w_in.npy
├── .gitignore                         # Git ignore file

How to run

  1. Clone the repository:
git clone https://github.com/tothantonio/heart-failure-analysis.git
cd heart-failure-analysis
  1. Install dependencies:
pip install -r requirements.txt
  1. Open the notebook:
jupyter notebook analiza_insuficiena_cardiaca.ipynb
  1. Follow the notebook steps to preprocess data, train the model, and evaluate results.

Source code

View the Heart Failure Analysis source on GitHub

Requirements

  • Python 3.8+
  • Jupyter Notebook
  • NumPy
  • Pandas
  • Scikit-learn
  • Matplotlib

Ethical considerations

  • Ensure the dataset does not contain personally identifiable information.
  • Avoid using the model for medical diagnosis without consulting healthcare professionals.
  • Be transparent about model limitations.

Future work

  • Test new neural network architectures to improve accuracy.
  • Add support for other heart health datasets.
  • Deploy the model as a simple web application for easier access.

License

This project is licensed under the MIT License. See the LICENSE file in the repository for details.