Multi-class valvular heart disease classification and severity detection with enhanced accuracy using convolutional neural network

Abstract

Aim: Valvular heart disease (VHD) is an important factor in cardiovascular mortality. While early diagnosis is essential, existing methods often lack accuracy, leading to misdiagnosis. This study explores the use of deep learning (DL) for multi-class classification of VHD using phonocardiograph (PCG) signals. Methods: The dataset includes normal PCG heart signals and nine VHD classes such as severe and mild aortic stenosis, mild and moderate mitral stenosis, mild, moderate, and severe mitral regurgitation, and moderate and severe tricuspid regurgitation. A novel convolutional neural network (CNN) is developed to classify these ten classes, demonstrating high accuracy even in noisy data acquisition. Unlike traditional machine learning, which relies on handcrafted features such as spectral, wavelet, and mel-frequency cepstral coefficients, the CNN model automatically learns patterns from raw signals, thereby reducing the need for manual feature engineering. The model is further validated using real-time PCG data from heart clinics diagnosed by heart specialists. Results: It achieves excellent performance with 99.81% accuracy, 99.84% precision, 99.82% recall, 99.85% F1-score, and an area under the curve of the receiver operating characteristic of 1.0. Conclusions: This DL model eliminates segmentation and manual pre-processing, enabling early, accurate VHD detection while outperforming existing methods and supporting clinical decisions and patient care.