❤️ Heart Health Detection

📝 Project Overview This project aims to develop a machine learning model to analyze heartbeat audio data and predict potential heart-related diseases. By utilizing audio data from two sets of heartbeat recordings, the model identifies patterns associated with different heart conditions.

📂 Dataset

• Folders: The dataset consists of two folders, set_a and set_b, containing heartbeat audio files.
• CSV Files:
  • set_a.csv: Metadata for set_a.
  • set_b.csv: Metadata for set_b.
  • set_a_timing.csv: Timing information for set_a recordings.

🎯 Objectives

1. Analyze and preprocess the audio files to extract meaningful features.
2. Train a machine learning model to classify heart health conditions.
3. Evaluate the model's performance and optimize it for better accuracy.

💻 Technologies Used

• Python
• OpenCV for image processing
• Librosa for audio processing
• Machine Learning Libraries: scikit-learn, TensorFlow/Keras

🔬 Methodology

1. Data Preprocessing:
   • Audio file loading and noise reduction.
   • Feature extraction using MFCCs, Spectrograms, etc.
2. Model Training:
   • Classification using CNN, RNN, or LSTM.
   • Evaluation through metrics like accuracy, precision, recall.
3. Testing and Optimization:
   • Cross-validation and hyperparameter tuning.

📊 Results

• Accuracy obtained: To be updated after testing.

🚀 Future Scope

• Integrating real-time heart monitoring systems.
• Enhancing the model to detect more complex heart conditions.

📚 References

• Heartbeat audio dataset sources
• Research papers on heart sound classification