An Improved Ensembled Deep Learning Techniques Detection and Prediction of Cardiovascular Disease
Keywords:
Cardiovascular Disease, Ensembled deep learning, CNN, RNN, LSTM, CVDAbstract
Globally, cardiovascular disease (CVD) is the leading cause of mortality. It can be prevented with early detection and treatment, which improves patient outcomes. CVD prediction can be enhanced with the help of deep learning (DL) techniques. Even with this, the disease's complexity and data availability may constrain these methods. This study proposes an enhanced ensembled DL technique for detecting and predicting CVD to address these concerns. The proposed methodology integrates several deep learning algorithms, thereby augmenting the precision of predictions. A dataset comprising patients with CVD and healthy controls was evaluated. The proposed procedure offers several benefits in comparison to current approaches. Firstly, it can acquire knowledge from an extensive dataset comprising patients with CVD and healthy controls, enabling it to detect patterns that may avoid individual DL algorithms. Secondly, the technique can improve accuracy by combining the assets of multiple DL algorithms. The outcomes demonstrated that the proposed method obtained a significantly higher accuracy of 94.5% than any deep learning algorithm. Extensive experimental trials revealed notable enhancements in the sensitivity, specificity, and accuracy of detection compared to baselines employing a singular model. The external validation and rigorous cross-validation of the ensemble's predictive capabilities on independent datasets demonstrated its potential for clinical implementation and generalizability. Using the proposed method, individualized treatment regimens for patients with CVD can be developed. It has the potential to save innumerable lives and revolutionize the detection and treatment of CVD.
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