Heart disease prediction using hybrid deep learning and medical imaging with wavelet-based feature extraction

The process of heart disease prediction is based on patient medical
information, which can be addressed in terms of medical image as well as
the results of an electrocardiogram (ECG) conducted to determine the risk of
developing heart disease. The hybrid deep learning (DL) algorithms are
developed using past data that can identify trends related to cardiovascular
disease (CVDs). In the current paper, it is possible to offer a new method of
heart disease prediction that would combine high-quality image processing
and hybrid DL to enhance the effectiveness of predictions and avoid the
shortcomings of the modern approaches. First, medical images like ECG
images are pre-processed with butterworth adaptive 2D wavelet filter, which
ensures maximal noise reduction, followed by maintenance of spatial and
frequency information. The Gabor Wavelet-based feature extraction
technique is applied to extract meaningful patterns, including both spatial
and frequency domain information, which is essential for detecting heart
related anomalies. The resultant features are then categorized, along with
both convolutional neural networks (CNN) and long short-term memory
(LSTM), to make reliable and precise predictions of heart disease. The
performance indicators, including accuracy (92.4%), precision (91.2%),
recall (93.5%), and F1-score (91.0%), are utilized. Applying the model
yields significant levels of reliability and generalization compared to
traditional applications.