Prediction of Heart Disease Using Deep Learning and Internet of Medical Things
Keywords:
Deep learning, Heart Diseases Diagnosis, Recurrent neural network, Convolution neural networkAbstract
The Internet of Medical Things (IoMT) devices have changed healthcare by providing continuous monitoring of patient physical data. In this case, the prompt and accurate diagnosis of cardiovascular diseases with the aid of focused training programmes has a great potential to enhance patient care. A thorough abstract of a ground-breaking work that predicts heart illness using deep learning and IoMT is presented in this article. In this study is concentrated on the creation and application of a cutting-edge deep learning framework especially created for the IoMT ecosystem's capacity for heart disease prediction. The suggested framework employs convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to the fullest extent possible to extract complex temporal dependencies from the physically heterogeneous data collected by IoMT devices. The biggest accomplishments was the creation of CNN-RNN's new hybrid architecture. This architecture has the ability to extract spatial and sequential characteristics from a variety of patient data flow. To enhance model generalisation, data from several IoMT sources, including pulse oximeters, electrocardiograms, and blood pressure monitors, are seamlessly incorporated. Additionally, the model has improved by the use of transference learning and previously instructed representations from associated medical fields.A large collection of real-world data is used to minutely analyse the proposed model. The results show that it is superior to earlier techniques in terms of anticipated precision and resistance. Additionally, the treatment processes give medical professionals crucial knowledge about the predictive factors that influence the model's judges, which enhances the model's interpretation.
Downloads
References
Muniasamy, A.; Tabassam, S.; Hussain, M.; Sultana, H.; Muniasamy, V.; Bhatnagar, R. Deep Learning for Predictive Analytics in Healthcare. In Advances in Intelligent Systems and Computing; Springer: Cham, Switzerland, 2019; pp. 32–42.
Smys, S. Survey on accuracy of predictive big data analytics in healthcare. J. Inf. Technol. Digit. World 2019, 01, 77–86.
Amin, P.; Anikireddypally, N.; Khurana, S.; Vadakkemadathil, S.; Wu, W. Personalized Health Monitoring Using Predictive Analytics. In Proceedings of the 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService), Newark, CA, USA, 4–9 April 2019.
Joseph, P.; Leong, D.; McKee, M.; Anand, S.S.; Schwalm, J.-D.; Teo, K.; Mente, A.; Yusuf, S. Reducing the Global Burden of Cardiovascular Disease, Part 1: The Epidemiology and Risk Factors: The Epidemiology and Risk Factors. Circ. Res. 2017, 121, 677–694.
Fuchs, F.D.; Whelton, P.K. High Blood Pressure and Cardiovascular Disease. Hypertension 2020, 75, 285–292.
S. Ajani and M. Wanjari, "An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering," 2013 5th International Conference and Computational Intelligence and Communication Networks, 2013, pp. 486-490, doi: 10.1109/CICN.2013.106.
Khetani, V. ., Gandhi, Y. ., Bhattacharya, S. ., Ajani, S. N. ., & Limkar, S. . (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253–262.
Sapp, P.A.; Riley, T.M.; Tindall, A.M.; Sullivan, V.K.; Johnston, E.A.; Petersen, K.S.; Kris-Etherton, P.M. Nutrition and Atherosclerotic Cardiovascular Disease. In Present Knowledge in Nutrition; Elsevier: Amsterdam, The Netherlands, 2020; pp. 393–411.
Cardiovascular Diseases Dataset.
https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset.
Moreno-Ibarra, M.; Villuendas-Rey, Y.; Lytras, M.; Yáñez-Márquez, C.; Salgado-Ramírez, J. Classification of Diseases Using Machine Learning Algorithms: A Comparative Study. Mathematics 2021, 9, 1817.
Bhatia, M.; Sood, S.K. Game Theoretic Decision Making in IoT-Assisted Activity Monitoring of Defence Personnel. Multimed. Tools Appl. 2017, 76, 21911–21935.
Firouzi, F.; Farahani, B.; Marinšek, A. The Convergence and Interplay of Edge, Fog, And Cloud in the AI-Driven Internet of Things (IoT). Inf. Syst. 2022, 107, 101840.
Biswas, A.R.; Giaffreda, R. IoT and Cloud Convergence: Opportunities and Challenges. In 2014 IEEE World Forum on Internet of Things (WF-IoT); IEEE: Manhattan, NY, USA, 2014.
Botta, A.; de Donato, W.; Persico, V.; Pescapé, A. Integration of Cloud Computing and Internet of Things: A Survey. Future Gener. Comput. Syst. 2016, 56, 684–700.
Santos, G.L.; Takako Endo, P.; Ferreira da Silva Lisboa Tigre, M.F.; Ferreira da Silva, L.G.; Sadok, D.; Kelner, J.; Lynn, T. Analyzing the Availability and Performance of an E-Health System Integrated with Edge, Fog and Cloud Infrastructures. J. Cloud Comput. Adv. Syst. Appl. 2018, 7, 16.
Suresh, S. Big Data and Predictive Analytics. Pediatr. Clin. N. Am. 2016, 63, 357–366.
Simpao, A.F.; Ahumada, L.M.; Gálvez, J.A.; Rehman, M.A. A Review of Analytics and Clinical Informatics in Health Care. J. Med. Syst. 2014, 38, 45.
Miotto, R.; Wang, F.; Wang, S.; Jiang, X.; Dudley, J.T. Deep Learning for Healthcare: Review, Opportunities and Challenges. Brief. Bioinform. 2018, 19, 1236–1246.
Pandey, S.; Janghel, R. Recent Deep Learning Techniques, Challenges and Its Applications for Medical Healthcare System: A Review. Neural Process. Lett. 2019, 50, 1907–1935.
S. Tuli, N. Basumatary, S. S. Gill, M. Kahani, R. C. Arya, G. S. Wander, and R. Buyya, ‘‘HealthFog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments,’’ Future Gener. Comput. Syst., vol. 104, pp. 187–200, Mar. 2020.
E. Choi, A. Schuetz, W. F. Stewart, and J. Sun, ‘‘Using recurrent neural network models for early detection of heart failure onset,’’ J. Amer. Med. Informat. Assoc., vol. 24, no. 2, pp. 361–370, Mar. 2017
S. M. Awan, M. U. Riaz, and A. G. Khan, ‘‘Prediction of heart disease using artificial neural networks,’’ VFAST Trans. Softw. Eng., vol. 13, no. 3, pp. 102–112, 2018.
Ahmed, H.; Younis, E.M.G.; Hendawi, A.; Ali, A.A. Heart Disease Identification from Patients’ Social Posts, Machine Learning Solution on Spark. Future Gener. Comput. Syst. 2020, 111, 714–722.
Kishore, A.H.N.; Jayanthi, V.E. Neuro-Fuzzy Based Medical Decision Support System for Coronary Artery Disease Diagnosis and Risk Level Prediction. J. Comput. Theor. Nanosci. 2018, 15, 1027–1037.
Dileep, P.; Rao, K.N.; Bodapati, P.; Gokuruboyina, S.; Peddi, R.; Grover, A.; Sheetal, A. An Automatic Heart Disease Prediction Using Cluster-Based Bi-Directional LSTM (C-BiLSTM) Algorithm. Neural Comput. Appl. 2022, 1–14.
Van Pham, H.; Son, L.H.; Tuan, L.M. A Proposal of Expert System Using Deep Learning Neural Networks and Fuzzy Rules for Diagnosing Heart Disease. In Frontiers in Intelligent Computing: Theory and Applications; Springer: Singapore, 2020; pp. 189–198.
Mehmood, A.; Iqbal, M.; Mehmood, Z.; Irtaza, A.; Nawaz, M.; Nazir, T.; Masood, M. Prediction of Heart Disease Using Deep Convolutional Neural Networks. Arab. J. Sci. Eng. 2021, 46, 3409–3422.
Jabeen, F.; Maqsood, M.; Ghazanfar, M.A.; Aadil, F.; Khan, S.; Khan, M.F.; Mehmood, I. An IoT Based Efficient Hybrid Recommender System for Cardiovascular Disease. Peer PeerNetw. Appl. 2019, 12, 1263–1276.
Muzammal, M.; Talat, R.; Sodhro, A.H.; Pirbhulal, S. A Multi-Sensor Data Fusion Enabled Ensemble Approach for Medical Data from Body Sensor Networks. Inf. Fusion 2020, 53, 155–164.
Khan, M.A. An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier. IEEE Access 2020, 8, 34717–34727.
Ali, F.; El-Sappagh, S.; Islam, S.M.R.; Kwak, D.; Ali, A.; Imran, M.; Kwak, K.-S. A Smart Healthcare Monitoring System for Heart Disease Prediction Based on Ensemble Deep Learning and Feature Fusion. Inf. Fusion 2020, 63, 208–222.
Chavhan, P. G., Patil, R. V. ., & Mahalle, P. N. (2023). Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 278–290. https://doi.org/10.17762/ijritcc.v11i4.6453
Thompson, A., Walker, A., Rodriguez, C., Silva, D., & Castro, J. Machine Learning Approaches for Sentiment Analysis in Social Media. Kuwait Journal of Machine Learning, 1(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/153
Dhabliya, D. Security analysis of password schemes using virtual environment (2019) International Journal of Advanced Science and Technology, 28 (20), pp. 1334-1339.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.