Adaptive Ensembled Fusion Based Deep CNN-Bilstm Model For Heart Disease Prediction In IoT

 Priyanka Dhaka^*1, Ruchi Sehrawat²

¹ Research Scholar Guru Gobind Singh Indraprastha University and Assistant Professor, MaharajaSurajmal Institute, GGSIPU, Delhi, India

²University School of Information and Communication Technology, GGSIPU, Delhi, India

Emails: priyankadhaka@msijanakpuri.com; ruchi.sehrawat@ipu.ac.in

Abstract

Internet-of-Things (IoT)-based heart disease prediction is a complex task and processing the real collected data directly for remote patient monitoring suffers from the limitations due to the irrelevant data features, affecting the prediction accuracy and raising the security concerns. Hence, the efficient Adaptive ensembled deep Convolution neural network –Bidirectional Long Short Term Memory (Adaptive ensembled deep CNN-BiLSTM ) classifier model is proposed via the fusion of interactive hunt-based CNN and Whale on Marine optimization (WoM)-based deep BiLSTM. The Adaptive optimization developed from the standard hybrid characteristics such as random searching, seeking, attack prohibition, following, and waiting characteristics optimized the fusion parameters of the developed classifier for attaining high detection accuracy. Additionally, the modified Elliptic Curve Cryptography (ECC) based Diffi-Huffman encryption algorithm provides the authentication and security of sensitive patient data in heart disease prediction. The developed model is evaluated with other competent methods in terms of accuracy, sensitivity, specificity as well as F-measure, which are reported as 97.573%, 98.012%, 97.592%, and 97.705% respectively.

Keywords: IoT; Smart healthcare monitoring; Elliptic Curve Cryptography; Convolution neural network; Diffi-Huffman encryption algorithm; Synthetic Minority Over-sampling Technique (SMOTE); Deep learning (DL).