An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model
Received: 12 October 2023 | Revised: 26 October 2023 | Accepted: 28 October 2023 | Online: 4 December 2023
Corresponding author: Priyanka Dhaka
Abstract
The increasing prevalence of cardiovascular disorders has created an imperative need for accurate diagnoses. Despite the emergence of numerous techniques for disease classification and secure data transmission, a prevailing shortcoming is the lack of precision in decision-making. This study aimed to address this critical issue by introducing an innovative disease prediction model that uses a hybrid classifier. The proposed hybrid classifier combined deep Bidirectional Long-Short-Term Memory (deep Bi LSTM) and deep Convolutional Neural Network (deep CNN).To further improve its performance, the proposed approach employed hybridized swarm optimization to fine-tune fusion parameters and optimize the learning model for enhanced accuracy. This study focused on heart disease as its central concern, strengthening data security through the implementation of Diffi-Huffman based on Elliptic Curve Cryptography (ECC) during data transmission. The resulting automatic disease prediction model adopted the hybrid deep classifier, which was born from the amalgamation of two components: the interactive hunt-deep CNN classifier and the WoM-deep Bi LSTM. The proposed hybrid learning model achieved impressive accuracy, F-measure, sensitivity, and specificity of 97.716%, 97.848%, 98.021%, and 97.807%, respectively, marking a significant advance in the realm of cardiovascular disease prediction.
Keywords:
cardiovascular disease prediction, elliptic curve cryptography, interactive hunt-deep CNN, WoM-deep bi LSTM, Diffi-HuffmanDownloads
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Copyright (c) 2023 Priyanka Dhaka , Ruchi Sehrawat, Priyanka Bhutani
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