Key result
The proposed Multi-Cascaded Deep Learning Network (MDLNet) with MI-FHCO feature selection achieved a best accuracy rate of 96.65% for heart disease prediction.
A novel hybrid deep learning model (MDLNet with MI-FHCO) achieved a 96.65% accuracy rate for heart disease prediction on a test dataset.
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Warrants caution against clinical adoption of AI models; leaves open need for prospective validation in heart disease prediction.
A novel deep learning based heart disease prediction model is proposed. Initially, the collected data is fed into the preprocessing phase using the NaN fill method. Then, the preprocessed data is given to the data transformation phase using data normalization approach. Further, the transformed data are fed into the optimal weighted feature selection process, which is selected by using the developed Mutated Iteration-based Fire Hawk with Coyote Optimization (MI-FHCO). Subsequently, heart disease is predicted by Multi-Cascaded Deep Learning Network (MDLNet). The best accuracy rate of the proposed approach is attained as 96.65% for dataset 4 to demonstrate its superior performance.
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Lakshmanan et al. (2025) studied Heart disease. Multi-Cascaded Deep Learning Network (MDLNet) with MI-FHCO feature selection was evaluated on Accuracy rate for heart disease prediction. The proposed Multi-Cascaded Deep Learning Network (MDLNet) with MI-FHCO feature selection achieved a best accuracy rate of 96.65% for heart disease prediction.
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