Heart Disease Prediction Using PCG Records with a Web Application for Diagnostic Insights

Abstract

A major contributor to deaths worldwide, heart disease causes public health systems under great strain and emphasizes medical resources. Reducing death and enhancing patient outcomes depend much on early detection; yet, present diagnostic techniques are usually expensive, out of reach, or need specialized knowledge and equipment. This project intends to provide a more affordable and easily available diagnostic tool, especially appropriate for use in resource-limited settings. The project addresses this difficulty by using Phonocardiogram (PCG) data recordings of heart sounds as a non-invasive input for heart disease prediction. Collected from several sites, PCG recordings were processed to lower noise and highlight diagnostically relevant characteristics. Techniques of feature extraction were used to identify audio patterns linked to cardiac anomalies. Trained and assessed were several machine learning and deep learning models including Support Vector Machines, Random Forests, and Convolutional Neural Networks. The best-performing model was included into a web-based application allowing users to submit their PCG files and get immediate diagnostic forecasts with related confidence scores. Initial system tests showed encouraging results; the model's accuracy was 91% and its AUC ROC score was 0.9753, indicating great dependability in differentiating between healthy and in danger human beings. Strong sensitivity and specificity were verified by the confusion matrix. These results suggest that the programme might be a useful and efficient early screening tool for heart disease. Its accuracy is being improved by more tests and optimizations, therefore guaranteeing more general real-world relevance.