“Lub and Dub”: An Optimized Approach Using Recurrent Neural Network for Classifying Heart Diseases Based on Heart Sound
Keywords:
Heart disease, classification, deep learning, LSTM, heart sounds.Abstract
Heart attack prediction is a critical task in cardiovascular healthcare, as early detection can significantly improve patient outcomes. Traditional systems diagnosed the disease based on statistical and images based, but these systems will not predict early. So our approach focuses on predicting heart attacks by analyzing systolic and diastolic heart sounds. The model employs refined deep learning techniques, specifically Long Short-Term Memory (LSTM) and Bi-LSTM models, to analyze heart sounds and capture irregularities in the "lub" and "dub" rhythm. Using a diverse dataset featuring heart sounds from various patients. And extracted multiple features like MFCC, frequency and mel spectrogram and stacked into single list to train these models. The model demonstrates exceptional performance with a notable classification accuracy of 0.90, signifying its effectiveness in precisely identifying heart diseases by recognizing irregular patterns.
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