Heart Sound Analysis for Disease Prediction

ML Project on Heart Disease Prediction using Heart Sounds (PCG)

We are looking to hire a freelancer who can help us build the complete end-to-end project, preferably someone with experience in audio processing and ML.

? Project Overview:
Input: Heart sound recordings (WAV format)
Output: Predict if the heart is normal or abnormal
Dataset: PhysioNet 2016 or similar PCG datasets
Goal: Build a working prediction system + basic frontend + report

✅ What You Need to Do:
Collect & preprocess heart sound data (noise removal, normalization)
Extract features (MFCCs, spectrogram, etc.)
Train and test ML/DL models (SVM, CNN, etc.)
Evaluate the model (accuracy, recall, confusion matrix)
Create a simple web app (Streamlit or Flask) for a demo

Provide:
Source code with comments
Project report (basic)
PPT for final presentation
Help us understand the code for viva (if needed)

? Skills Required:
Python, Librosa, NumPy, Pandas
scikit-learn / TensorFlow / Keras
Audio feature extraction (MFCC, spectrograms)
Basic web deployment (Streamlit or Flask)
Bonus: Experience with medical datasets or PCG analysis

? Deadline:
Preferred delivery: Within 30 days

? Budget:
₹6,000 to ₹7,000 (negotiable based on experience and work quality)