238 215
Full Length Article
Fusion: Practice and Applications
Volume 14 , Issue 1, PP: 40-55 , 2024 | Cite this article as | XML | Html |PDF

Title

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

  Priyanka Dhaka 1 * ,   Ruchi Sehrawat 2

1  Research Scholar Guru Gobind Singh Indraprastha University and Assistant Professor, MaharajaSurajmal Institute, GGSIPU, Delhi, India
    (priyankadhaka@msijanakpuri.com)

2  University School of Information and Communication Technology, GGSIPU, Delhi, India
    (ruchi.sehrawat@ipu.ac.in)

Full Text Download


Doi   :   https://doi.org/10.54216/FPA.140104

Received: June 01, 2023 Revised: September 04, 2023 Accepted: November 04, 2023

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).

References :

[1] Diller, G.P., Kempny, A., Babu-Narayan, S.V., Henrichs, M., Brida, M., Uebing, A., Lammers, A.E., Baumgartner, H., Li, W., Wort, S.J. and Dimopoulos, K., Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients. European heart journal, 40(13), 1069-1077, 2019.

[2] Junejo, A.R., Shen, Y., Laghari, A.A., Zhang, X. and Luo, H., Notice of Retraction: Molecular Diagnostic and Using Deep Learning Techniques for Predict Functional Recovery of Patients Treated of Cardiovascular Disease. IEEE Access, 7, 120315-120325, 2019.

[3] Pan, Y., Fu, M., Cheng, B., Tao, X., and Guo, J., Enhanced deep learning assisted convolutional neural network for heart disease prediction on the internet of medical things platform. Ieee Access, 8, 189503-189512, 2020.

[4] Poplin, R., Varadarajan, A.V., Blumer, K., Liu, Y., McConnell, M.V., Corrado, G.S., Peng, L. and Webster, D.R., Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature biomedical engineering, 2(3), 158-164, 2018.

[5]  Zhang, Z., Qiu, Y., Yang, X. and Zhang, M., Enhanced character-level deep convolutional neural networks for cardiovascular disease prediction. BMC medical informatics and decision making, 20(3), 1-10, 2020.

 [6] Sarmah, S.S., An efficient IoT-based patient monitoring and heart disease prediction system using deep learning modified neural network. Ieee access, 8, 135784-135797, 2020.

 [7] Jha, S., Artificial intelligence and Internet of Things for sustainable healthcare system. Journal of Sustainable Science and Transformative Research-Reviews & Letters, 1(2), 44-50, 2022.

 [8] Ha, W. and Vahedi, Z., Automatic breast tumor diagnosis in MRI based on a hybrid CNN and feature-based method using improved deer hunting optimization algorithm. Computational Intelligence and Neuroscience, 2021.

 [9]Nancy, A.A., Ravindran, D., Raj Vincent, P.D., Srinivasan, K. and Gutierrez Reina, D., Iot-cloud-based smart healthcare monitoring system for heart disease prediction via deep learning. Electronics, 11(15), 2292, 2022.

 [10]Yang, X. and Li, S., Prediction of COVID-19 Using a WOA-BILSTM Model. Bioengineering, 10(8), 883, 2023

[11]  Dang, L.M., Piran, M.J., Han, D., Min, K. and Moon, H., A survey on internet of things and cloud computing for healthcare. Electronics, 8(7), 768, 2019.

 [12]Ahmed, F., An Internet of Things (IoT) application for predicting the quantity of future heart attack patients. International Journal of Computer Applications, 164(6), 36-40, 2017.

 [13] Sidheeque, A., Kumar, A., Balamurugan, R., Deepak, K.C. and Sathish, K., Heartbeat sensing and heart attack detection using Internet of Things: IoT. Int. J. Eng. Sci. Comput, 7(4), 6662-6666, 2017.

 [14] Khan, M.A., An IoT framework for heart disease prediction based on MDCNN classifier. IEEE Access, 8, 34717-34727, 2020.

 [15] Lim, T.S., Loh, W.Y. and Shih, Y.S., A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-three Old and New Classification Algorithms, Department of Statistics, University of Wisconson, Madison, 1999.

 [16]Datta, P. and Sharma, B., A survey on IoT architectures, protocols, security and smart city based applications. In 2017 8th International conference on computing, communication and networking technologies (ICCCNT). 1-5, 2017

 [17] Tuli, S., Basumatary, N. and Buyya, R., Edgelens: Deep learning based object detection in integrated iot, fog and cloud computing environments. In 2019 4th International Conference on Information Systems and Computer Networks (ISCON) 496-502, 2019,

 [18] Verma, P., Tiwari, R., Hong, W.C., Upadhyay, S. and Yeh, Y.H., FETCH: a deep learning-based fog computing and IoT integrated environment for healthcare monitoring and diagnosis. IEEE Access, 10, 12548-12563, 2022.

 [19] https://www.kaggle.com/sid321axn/heart-statlog-cleveland-hungary-final

 [20] https://archive.ics.uci.edu/ml/datasets/heart+disease

 [21] Rosy, J.V. and Kumar, S.B.R., Optimized encryption based elliptical curve Diffie-Hellman approach for secure heart disease prediction. International Journal of Advanced Technology and Engineering Exploration, 8(83), 1367, 2021.

 [22] Barani, A.M., Latha, R. and Manikandan, R., Implementation of Artificial Fish Swarm Optimization for Cardiovascular Heart Disease. International Journal of Recent Technology and Engineering (IJRTE), 8(4S5), 134-136, 2019.

 [23] Zhang, M., Wen, G. and Yang, J., Duck swarm algorithm: a novel swarm intelligence algorithm. arXiv preprint arXiv:2112.13508. 2021.

 [24] Pati, A., Parhi, M., Alnabhan, M., Pattanayak, B. K., Habboush, A. K., & Al Nawayseh, M. K. (2023, February). An IoT-Fog-Cloud Integrated Framework for Real-Time Remote Cardiovascular Disease Diagnosis. In Informatics (Vol. 10, No. 1, p. 21). MDPI.

 [25]  Sarmah, S.S., An efficient IoT-based patient monitoring and heart disease prediction system using deep learning modified neural network. Ieee access, 8, 135784-135797, 2020.

[26] Tuli, S., Basumatary, N., Gill, S.S., Kahani, M., Arya, R.C., Wander, G.S. and Buyya, R., HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments. Future Generation Computer Systems, 104, 187-200, 2020.

[27] Maldonado, S., Julio L., and Carla V., An alternative SMOTE oversampling strategy for high-dimensional datasets. Applied Soft Computing 76, 380-389, 2019

 [28] Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H. and Raad, A., Reviewing Multimodal Machine Learning and Its Use in Cardiovascular Diseases Detection. Electronics, 12(7), 1558, 2023.

 

[29] Nadeem, M. W., Goh, H. G., Khan, M. A., Hussain, M., & Mushtaq, M. F. (2021). Fusion-Based Machine Learning Architecture for Heart Disease Prediction. Computers, Materials & Continua, 67(2).

 

 

Cite this Article as :
Style #
MLA Priyanka Dhaka, Ruchi Sehrawat. "Adaptive Ensembled Fusion Based Deep CNN-Bilstm Model For Heart Disease Prediction In IoT." Fusion: Practice and Applications, Vol. 14, No. 1, 2024 ,PP. 40-55 (Doi   :  https://doi.org/10.54216/FPA.140104)
APA Priyanka Dhaka, Ruchi Sehrawat. (2024). Adaptive Ensembled Fusion Based Deep CNN-Bilstm Model For Heart Disease Prediction In IoT. Journal of Fusion: Practice and Applications, 14 ( 1 ), 40-55 (Doi   :  https://doi.org/10.54216/FPA.140104)
Chicago Priyanka Dhaka, Ruchi Sehrawat. "Adaptive Ensembled Fusion Based Deep CNN-Bilstm Model For Heart Disease Prediction In IoT." Journal of Fusion: Practice and Applications, 14 no. 1 (2024): 40-55 (Doi   :  https://doi.org/10.54216/FPA.140104)
Harvard Priyanka Dhaka, Ruchi Sehrawat. (2024). Adaptive Ensembled Fusion Based Deep CNN-Bilstm Model For Heart Disease Prediction In IoT. Journal of Fusion: Practice and Applications, 14 ( 1 ), 40-55 (Doi   :  https://doi.org/10.54216/FPA.140104)
Vancouver Priyanka Dhaka, Ruchi Sehrawat. Adaptive Ensembled Fusion Based Deep CNN-Bilstm Model For Heart Disease Prediction In IoT. Journal of Fusion: Practice and Applications, (2024); 14 ( 1 ): 40-55 (Doi   :  https://doi.org/10.54216/FPA.140104)
IEEE Priyanka Dhaka, Ruchi Sehrawat, Adaptive Ensembled Fusion Based Deep CNN-Bilstm Model For Heart Disease Prediction In IoT, Journal of Fusion: Practice and Applications, Vol. 14 , No. 1 , (2024) : 40-55 (Doi   :  https://doi.org/10.54216/FPA.140104)
↑