2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 15 , Issue 1 , PP: 08-18, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Urvashi Gupta 1 , Rohit Sharma 2 *
Doi: https://doi.org/10.54216/FPA.150101
Wireless Body Sensor Network (BSN) comprises wearables with different sensing, processing, storing, and broadcast abilities. Once several devices acquire the data, multi-sensor fusion was needed for transforming erroneous sensor information into maximum quality fused data. Deep learning (DL) approaches are utilized in different application domains comprising e-health for applications like activity detection, and disease forecast. In recent times, it can be demonstrated that the accuracy of classification techniques is enhanced by the combination of feature selection (FS) approaches. This article develops a Multi-sensor Data Fusion based Medical Data Classification Model using Gorilla Troops Optimization with Deep Learning (MDFMDC-GTODL) algorithm. The proposed MDFMDC-GTODL method enables collection of various daily activity data using different sensors, which are then fused to produce high-quality activity data. In addition, the MDFMDC-GTODL technique applies optimal attention based bidirectional long short term memory (ABLSTM) for heart disease prediction. In this study, Gorilla Troops Optimization Algorithm based FS (GTOA-FS) technique is involved to improve the classification performance. The simulation outcome of the MDFMDC-GTODL technique are validated and the results are investigated in different prospects. A wide-ranging simulation analysis stated the better performance of the MDFMDC-GTODL method over other compared approaches.
Sensor applications , Medical data , Data fusion , Deep learning , Feature selection
[1] Khan, A.H., Hussain, M. and Malik, M.K., 2021. Cardiac disorder classification by electrocardiogram sensing using deep neural network. Complexity, 2021.
[2] Ganesan, M. and Sivakumar, N., 2019, March. IoT based heart disease prediction and diagnosis model for healthcare using machine learning models. In 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1-5). IEEE.
[3] Vijayaganth, V. and Naveenkumar, M., 2021. 4 Smart Sensor Based Prognostication of Cardiac Disease Prediction Using Machine Learning Techniques. In Applications of Machine Learning in Big-Data Analytics and Cloud Computing (pp. 63-80). River Publishers.
[4] Manimurugan, S., Almutairi, S., Aborokbah, M.M., Narmatha, C., Ganesan, S., Chilamkurti, N., Alzaheb, R.A. and Almoamari, H., 2022. Two-Stage Classification Model for the Prediction of Heart Disease Using IoMT and Artificial Intelligence. Sensors, 22(2), p.476.
[5] Jabeen, F., Maqsood, M., Ghazanfar, M.A., Aadil, F., Khan, S., Khan, M.F. and Mehmood, I., 2019. An IoT based efficient hybrid recommender system for cardiovascular disease. Peer-to-Peer Networking and Applications, 12(5), pp.1263-1276.
[6] Mehmood, A., Iqbal, M., Mehmood, Z., Irtaza, A., Nawaz, M., Nazir, T. and Masood, M., 2021. Prediction of heart disease using deep convolutional neural networks. Arabian Journal for Science and Engineering, 46(4), pp.3409-3422.
[7] Patil, R.S. and Gangwar, M., 2022. Heart Disease Prediction Using Machine Learning and Data Analytics Approach. In Proceedings of International Conference on Communication and Artificial Intelligence (pp. 351-361). Springer, Singapore.
[8] Maurya, M.R., Riyaz, N.U., Reddy, M., Yalcin, H.C., Ouakad, H.M., Bahadur, I., Al-Maadeed, S. and Sadasivuni, K.K., 2021. A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring. Medical & Biological Engineering & Computing, 59(11), pp.2185-2203.
[9] Kalid, N., Zaidan, A.A., Zaidan, B.B., Salman, O.H., Hashim, M., Albahri, O.S. and Albahri, A.S., 2018. Based on real time remote health monitoring systems: a new approach for prioritization “large scales data” patients with chronic heart diseases using body sensors and communication technology. Journal of medical systems, 42(4), pp.1-37.
[10] Basheer, S., Alluhaidan, A.S. and Bivi, M.A., 2021. Real-time monitoring system for early prediction of heart disease using Internet of Things. Soft Computing, 25(18), pp.12145-12158.
[11] Ali, F., El-Sappagh, S., Islam, S.R., Kwak, D., Ali, A., Imran, M. and Kwak, K.S., 2020. A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Information Fusion, 63, pp.208-222.
[12] Muzammal, M., Talat, R., Sodhro, A.H. and Pirbhulal, S., 2020. A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks. Information Fusion, 53, pp.155-164.
[13] Al-Makhadmeh, Z. and Tolba, A., 2019. Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: A classification approach. Measurement, 147, p.106815.
[14] Pradhan, M.R., Mago, B. and Ateeq, K., 2022. A classification-based sensor data processing method for the internet of things assimilated wearable sensor technology. Cluster Computing, pp.1-16.
[15] Khan, M.A. and Algarni, F., 2020. A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS. IEEE Access, 8, pp.122259-122269.
[16] Khan, M.A., 2020. An IoT framework for heart disease prediction based on MDCNN classifier. IEEE Access, 8, pp.34717-34727.
[17] Yoo, H., Han, S. and Chung, K., 2020, July. A frequency pattern mining model based on deep neural network for real-time classification of heart conditions. In Healthcare (Vol. 8, No. 3, p. 234). MDPI.
[18] Ginidi, A., Ghoneim, S.M., Elsayed, A., El-Sehiemy, R., Shaheen, A. and El-Fergany, A., 2021. Gorilla troops optimizer for electrically based single and double-diode models of solar photovoltaic systems. Sustainability, 13(16), p.9459.
[19] Al Banna, M.H., Ghosh, T., Al Nahian, M.J., Taher, K.A., Kaiser, M.S., Mahmud, M., Hossain, M.S. and Andersson, K., 2021. Attention-based bi-directional long-short term memory network for earthquake prediction. IEEE Access, 9, pp.56589-56603.
[20] Yang, E., Shankar, K., Kumar, S., Seo, C., & Moon, I. (2023). Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images. Biomedicines, 11(12), 3200.
[21] B. Narasimha Swamy, Rajeswari Nakka, Aditi Sharma, S. Phani Praveen, Venkata Nagaraju Thatha, Kumar Gautam. (2023). An Ensemble Learning Approach for detection of Chronic Kidney Disease (CKD). Journal of Journal of Intelligent Systems and Internet of Things, 10 ( 2 ), 38-48 (Doi : https://doi.org/10.54216/JISIoT.100204)
[22] A. Yuva Krishna, K. Ravi Kiran, N. Raghavendra Sai, Aditi Sharma, S. Phani Praveen, Jitendra Pandey. (2023). Ant Colony Optimized XGBoost for Early Diabetes Detection: A Hybrid Approach in Machine Learning. Journal of Journal of Intelligent Systems and Internet of Things, 10 ( 2 ), 76-89 (Doi : https://doi.org/10.54216/JISIoT.100207)
