A Novel Deep Learning Model for Cardiovascular Disease Prediction (MarCDP)
Keywords:
Recurrent Neural Networks (RNN), Feature selection (LASSO), Synthetic Minority Over-sampling Technique (SMOTE), prediction accuracy.Abstract
Cardiovascular disease (CVD) is one of the leading causes of death worldwide, highlighting the critical need for efficient early identification. Early and accurate prediction is crucial for effective treatment. Despite various proposed solutions, a gap in prediction accuracy still persists. Therefore, this study introduces a novel deep learning model designed to enhance CVD prediction by utilization several advanced techniques. The model of Marwa Cardiovascular disease predication (MarCDP) leverages Recurrent Neural Networks (RNN) to capture patterns, employs Least Absolute Shrinkage and Selection Operator (LASSO) for feature selection, and utilizes the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. A range of optimization approaches is applied to fine-tune the model’s parameters, resulting in improved accuracy. The model was developed and evaluated using four benchmark datasets: Cleveland, Hungary, Switzerland, and Long Beach V. The proposed model achieved an accuracy of 98.05%, surpassing the performance of existing deep learning models. This novel approach offers a promising product for early CVD detection.
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