Design of an Augmented Varma GRU & LSTM Based Multimodal Feature Analysis Model for Enhancing Heart Disease Preemption Performance
Keywords:
ECG, Classification, Diseases, Multiclass, Fourier, Cosine, iVector, Gabor, Wavelet, GRO, ILM, ScenariosAbstract
The main cause of death in the world is heart disease. Preemption and early detection can drastically lower the fatality rate. In this research, we suggest an improved multimodal feature analysis model for heart disease prevention based on augmented VARMA GRU & LSTM. This study is necessary since existing preemption models are not very accurate, especially when processing samples from multimodal datasets.For the purpose of capturing time series and nonlinear interactions between the features, the suggested model integrates VARMA, LSTM, and GRU models. For feature categorization, a customised 1D CNN is also used. We combine the Laplacian Transform, Gabor Transform, and Contourlet Transform to extract the features. The suggested model is then given the multimodal data, producing an expanded set of preemption predictions.A publicly available dataset on heart illness is used to test the suggested model, and the findings reveal that it performs better than current preemption models in terms of accuracy, sensitivity, and specificity. The proposed model has a 96.7% overall accuracy, a 96.3% sensitivity, and a 96.9% specificity. The outcomes show how well the suggested approach handles multimodal data and raises the bar for heart disease prevention capability.In summary, the suggested model offers a notable enhancement in heart disease preemption performance and offers a fresh method for managing multimodal data. The success of the suggested model for various use scenarios is largely due to the combination of VARMA, LSTM, and GRU models, coupled with a tailored 1D CNN and feature extraction algorithms.
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