Health Conditions Prediction in Cardiac Patient Using Deep Ensemble Learning Based IoT Systems
Keywords:
IoT, Health condition, Deep learning, predictionAbstract
The ongoing transformation of the Internet of Things (IoT) is profoundly impacting businesses promoting healthier lifestyles through technology. This paper introduces a novel system utilizing machine learning to extract features from long-term health data, particularly beneficial for individuals with chronic illnesses. The prototype presented suggests potential for more affordable and effective healthcare, encouraging the medical industry to adopt and test such devices. By leveraging big data architecture, artificial intelligence, and IoT, the proposed system forecasts illness progression, a development with significant implications for healthcare. Employing data mining techniques, including genetic algorithms, the framework optimizes feature selection for real-time medical inputs. A proposed ensemble framework integrates various algorithms enhancing prediction accuracy and robustness. Training each algorithm individually and combining predictions through weighted averaging or voting results in a more reliable ensemble forecast. The ensemble DBN framework, incorporating multiple algorithmic predictions, demonstrates superior accuracy and resilience compared to individual algorithms.
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Almulihi, A., Saleh, H., Hussien, A. M., Mostafa, S., El-Sappagh, S., Alnowaiser, K., ... & Refaat Hassan, M. (2022). Ensemble learning based on hybrid deep learning model for heart disease early prediction. Diagnostics, 12(12), 3215.
Sivasankari, S. S., Surendiran, J., Yuvaraj, N., Ramkumar, M., Ravi, C. N., & Vidhya, R. G. (2022, April). Classification of diabetes using multilayer perceptron. In 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) (pp. 1-5). IEEE.
Rath, A., Mishra, D., Panda, G., Satapathy, S. C., & Xia, K. (2022). Improved heart disease detection from ECG signal using deep learning based ensemble model. Sustainable Computing: Informatics and Systems, 35, 100732.
Yuvaraj, N., Raja, R. A., Kousik, N. V., Johri, P., & Diván, M. J. (2020). Analysis on the prediction of central line-associated bloodstream infections (CLABSI) using deep neural network classification. In Computational intelligence and its applications in healthcare (pp. 229-244). Academic Press.
Nancy, A. A., Ravindran, D., Raj Vincent, P. D., Srinivasan, K., & Gutierrez Reina, D. (2022). Iot-cloud-based smart healthcare monitoring system for heart disease prediction via deep learning. Electronics, 11(15), 2292.
Manikandan, R., Sara, S. B. V. J., Yuvaraj, N., Chaturvedi, A., Priscila, S. S., & Ramkumar, M. (2022, May). Sequential pattern mining on chemical bonding database in the bioinformatics field. In AIP Conference Proceedings (Vol. 2393, No. 1). AIP Publishing.
Ganie, S. M., Pramanik, P. K. D., Malik, M. B., Nayyar, A., & Kwak, K. S. (2023). An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms. Comput. Syst. Sci. Eng., 46(3), 3993-4006.
Kannan, S., Yuvaraj, N., Idrees, B. A., Arulprakash, P., Ranganathan, V., Udayakumar, E., & Dhinakar, P. (2021). Analysis of convolutional recurrent neural network classifier for COVID-19 symptoms over computerised tomography images. International Journal of Computer Applications in Technology, 66(3-4), 427-432.
Yashudas, A., Gupta, D., Prashant, G. C., Dua, A., AlQahtani, D., & Reddy, A. S. K. (2024). DEEP-CARDIO: Recommendation System for Cardiovascular Disease Prediction using IOT Network. IEEE Sensors Journal.
Gowrishankar, J., Narmadha, T., Ramkumar, M., & Yuvaraj, N. (2020). Convolutional neural network classification on 2d craniofacial images. International Journal of Grid and Distributed Computing, 13(1), 1026-1032.
Alsuhibany, S. A., Abdel-Khalek, S., Algarni, A., Fayomi, A., Gupta, D., Kumar, V., & Mansour, R. F. (2021). Ensemble of deep learning based clinical decision support system for chronic kidney disease diagnosis in medical internet of things environment. Computational Intelligence and Neuroscience, 2021.
Yuvaraj, N., Praghash, K., Arshath Raja, R., Chidambaram, S., & Shreecharan, D. (2022, December). Hyperspectral image classification using denoised stacked auto encoder-based restricted Boltzmann machine classifier. In International Conference on Hybrid Intelligent Systems (pp. 213-221). Cham: Springer Nature Switzerland.
Gao, X. Y., Amin Ali, A., Shaban Hassan, H., & Anwar, E. M. (2021). Improving the accuracy for analyzing heart diseases prediction based on the ensemble method. Complexity, 2021, 1-10.
Shorewala, V. (2021). Early detection of coronary heart disease using ensemble techniques. Informatics in Medicine Unlocked, 26, 100655.
Aldahiri, A., Alrashed, B., & Hussain, W. (2021). Trends in using IoT with machine learning in health prediction system. Forecasting, 3(1), 181-206.
Raju, K. B., Dara, S., Vidyarthi, A., Gupta, V. M., & Khan, B. (2022). Smart heart disease prediction system with IoT and fog computing sectors enabled by cascaded deep learning model. Computational Intelligence and Neuroscience, 2022.
Swathy, M., & Saruladha, K. (2022). A comparative study of classification and prediction of Cardio-Vascular Diseases (CVD) using Machine Learning and Deep Learning techniques. ICT Express, 8(1), 109-116.
Raeesi Vanani, I., & Amirhosseini, M. (2021). IoT-based diseases prediction and diagnosis system for healthcare. Internet of Things for Healthcare Technologies, 21-48.
https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset
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Copyright (c) 2024 C. Veera Prakash Kumar, T. S. Baskaran
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