Heart health fog: A deep learning approach leveraging Internet of Things and fog computing for real-time heart disease prediction

• Autorzy: Jesudoss Angela Jennifa Sujana , Mystica Infant , Rajesh Jebasingh Jeremiah , Stebel Krzysztof

Identyfikatory

DOI

10.12913/22998624/204392

• Języki publikacji: EN

Abstrakty

EN

Real-time data available from IoT devices, allows predicting a patient's risk of heart disease using Health Fog. The proposed approach is economically affordable yet reliable, as it offers low-cost input devices to meet the necessary requirements. The goal achieved is accurate results with low latency. The reduction in latency time was achieved through fog processing. The Arduino UNO serves as an IoT device and the Raspberry Pi serves as a node in the fog architecture. The centralised cloud system has its limitations in terms of direct access, hence the use of a distributed multilayer model significantly improves the availability of computing resources locally where they are needed. Heart disease is predicted using a deep learning algorithm. The DNN algorithm is used by the model to improve performance. The data is sent and received by an Android mobile phone, a fog node that completes the forecast sends it back. Deep learning is accepted for predicting outcomes in the healthcare industry because it is characterised by precise results. Requests are sent between the client and server computers via the REST API. As a result, data is digitally stored in the cloud for later use. The model makes use of Bagging Classifier ensemble learning. Based on a deep learning algorithm, the user submitting input for heart disease prediction gets a result with high reliability.

Słowa kluczowe

EN

heart disease prediction; internet of things; IoT; fog computing; deep learning; edge computing; predictive analytics

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2025
• Tom: Vol. 19, no. 7
• Strony: 406--414
• Opis fizyczny: Bibliogr. 24 poz., fig., tab.

Twórcy

autor

Jesudoss Angela Jennifa Sujana

• Department of Artificial Intelligence and Data Science, Mepco Schlenk Engineering College, Sivakasi 626005, Virudhunagar, India

autor

Mystica Infant

• Full Stack Developer, TranslateLive, Florida, United States of America

autor

Rajesh Jebasingh Jeremiah

• Department of Computer Engineering, Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, Proszkowska 76, 45-758 Opole, Poland

autor

Stebel Krzysztof

• Faculty of Automatic Control, Electronics and Computer Science, Department of Automatic Control and Robotics, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

• https://orcid.org/0000-0003-2912-6742

Bibliografia

• 1. Maqbool S., Iqbal M.W., Naqvi M.R., Arif K.S., Ahmed M., Arif M. IoT based remote patient monitoring system. 2020 International Conference on Decision Aid Sciences and Application. 2020, 1255–1260.
• 2. Sujana J.A.J., Revathi T., Malarvizhili M. 2015. Scheduling of scientific workflows in cloud with replication. Applied Mathematical Sciences, 2015; 9(46), 2273–2280. http://dx.doi.org/10.12988/ams.2015.53208.
• 3. Angela Jennifa Sujana, J., Revathi, T., Joshua Rajanayagam, S. Fuzzy-based security-driven optimistic scheduling of scientific workflows in cloud computing. IETE Journal of Research, 2018; 66(2): 224–241. https://doi.org/10.1080/03772063.2018.1486740.
• 4. Soniya J., Sujana J.A.J., Revathi T. Dynamic fault tolerant scheduling mechanism for real time tasks in cloud computing. International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, 2016; 124–129. https://doi.org/10.1109/ICEEOT.2016.7754872.
• 5. Angela Jennifa Sujana, J., Geethanjali, M., Venitta Raj, R., Revathi, T. Trust model based scheduling of stochastic workflows in cloud and fog computing. In: Das, H., Barik, R., Dubey, H., Roy, D. (eds) Cloud Computing for Geospatial Big Data Analytics. Studies in Big Data. Springer, Cham., 2019; 49. https://doi.org/10.1007/978-3-030-03359-0_2.
• 6. Sujana, J.A.J., Revathi, T., Priya, T.S.S. et al. Smart PSO-based secured scheduling approaches for scientific workflows in cloud computing. Soft Comput; 2019; 23: 1745–1765. https://doi.org/10.1007/s00500-017-2897-8.
• 7. Islam, S.M.R., Kwak, D., Kabir, M.H., Hossain, M., Kwak, K.S. The internet of things for health care: a comprehensive survey. IEEE Access, 2015; 3: 678–708.
• 8. Bodora D., Kłopot T., Stebel K. Adaptive DMC controller based on cloud computing. Przegląd Elektrotechniczny, 12/2024; 100, 48–52. https://doi.org/10.15199/48.2024.12.11.
• 9. Mahmud, R., Kotagiri, R., Buyya, R. 2018. Fog computing: A taxonomy, survey, and future directions. Internet of Things, 1–2, 89–112.
• 10. Saad A., Sheikh U. U., Moslim M. S. Developing convolutional neural network for recognition of bone fractures in x-ray images. Advances in Science and Technology Research Journal 2024; 18(4): 228–237. https://doi.org/10.12913/22998624/188656.
• 11. Miotto, R., Li, L., Kidd, B.A., Dudley, J.T. Deep Patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, 2017; 6: 26094.
• 12. Acharya, U.R., Fujita, H., Lih, O.S., Hagiwara, Y., Tan, J.H., Adam, M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Information Sciences, 2017; 405: 81–90.
• 13. Nogay H.S., Tahir Cetin Akinci T.C., Yilmaz M. Comparative experimental investigation and application of five classic pre-trained deep convolutional neural networks via transfer learning for diagnosis of breast cancer. Advances in Science and Technology Research Journal 2021; 15(3): 1–8. https://doi.org/10.12913/22998624/137964.
• 14. Rahmani, A.M., Liljeberg, P., Jantsch, A. Fog computing in the Internet of Things: Intelligence at the edge. Springer, 2018.
• 15. Oussous, A., Benjelloun, F.Z., Ait Lahcen, A., Belfkih, S. Big data technologies: A survey. Journal of King Saud University-Computer and Information Sciences, 2018; 30(4): 431–448.
• 16. Shi, W., et al. Edge computing: vision and challenges. IEEE Internet of Things Journal, 2016.
• 17. Yazici, A., Tiwari, P., Bhatnagar, V. Fog computing for healthcare internet of things: a review. Future Generation Computer Systems, 2019.
• 18. Rajput, D.S., Javaid, N., Nazir, A., Alrajeh, N. A deep learning approach for prediction of heart diseases. Computer Methods and Programs in Biomedicine; 2020.
• 19. Dietterich, T.G. Ensemble methods in machine learning. Multiple Classifier Systems; 2000.
• 20. Mienye, I.D., Sun, Y., Wang, Z. An improved ensemble learning approach for the prediction of heart disease risk. Informatics in Medicine Unlocked, 2020. https://doi.org/10.1016/j.imu.2020.100402.
• 21. Varghese, B., Buyya, R. Next generation cloud computing: New trends and research directions. Future Generation Computer Systems, 2018.
• 22. Alrawais, A., et al. Fog computing for the Internet of Things: security and privacy issues. IEEE Internet Computing, 2017.
• 23. Tuli S., Basumatary N., Gill S.S., Kahani M., Arya R.C., Wander G.S., 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, 2020; 104: 187–200.
• 24. Velychko D., Osukhivska H., Palaniza Y., Lutsyk N., Sobaszek Ł. Artificial intelligence based emergency identification computer system. Advances in Science and Technology Research Journal 2024; 18(2): 296–304. https://doi.org/10.12913/22998624/184343.