Intelligence Furnished by Fog Intelligent Clinical Decision Support System for Healthcare Applications (FICDSS)
Keywords:
Clinical Decision Support System, Fog Computing,Healthcare,Internet of Things, Fuzzy Logic System, Machine Learning SystemAbstract
Wearable devices are widely utilised in intelligent healthcare systems. Recognising and understanding physiological data from medical sensor equipment is crucial for smart healthcare. Fog computing analyses physiological data to reduce cloud computing's latency. Smart healthcare, however, has substantial hurdles in a fog environment because to delay for emergency health status and overloading. Here, we offer the first ever Fog-enabled Intelligence Clinical Decision Support System (FICDSS) for detecting physiological parameters. The goal of this system is to increase the efficiency of medical treatment and diagnosis by employing intelligent systems. The suggested system is composed of four distinct layers: the sensor layer, the edge layer, the fog layer, and the cloud layer. At the edge layer, a microcontroller unit receives data from wearable health sensors. At the fog layer, an intelligence system is deployed with fuzzy logic and machine learning systems based on the context and type of data that predicts the health diagnosis. At the cloud level, the results of the sensors and the most up-to-date information are shown. Both the cloud and fog layers adjust rapidly to the user's vitals. The proposed experimental simulation is built and analysed based on several analytical parameters, detection accuracy, and latency.
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Adi, E., Anwar, A., Baig, Z. et al., “Machine learning and data analytics for the IoT”, 2020, Neural Comput&Applic 32, 16205–16233. https://doi.org/10.1007/s00521-020-04874-y
Luca Greco, GennaroPercannella, PierluigiRitrovato, Francesco Tortorella, Mario Vento, “Trends in IoT based solutions for health care: Moving AI to the edge”, 2020 Pattern Recognition Letters, Volume 135, Pages 346-353, ISSN 0167-8655, https://doi.org/10.1016/j.patrec.2020.05.016.
BaharFarahani, FarshadFirouzi, Victor Chang, Mustafa Badaroglu, Nicholas Constant, KunalMankodiya, “Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare”, Future Generation Computer Systems, Volume 78, Part 2, 2018, Pages 659-676, ISSN 0167-739X, https://doi.org/10.1016/j.future.2017.04.036.
M. Asif-Ur-Rahman et al., "Toward a Heterogeneous Mist, Fog, and Cloud-Based Framework for the Internet of Healthcare Things," in IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4049-4062, June 2019, doi: 10.1109/JIOT.2018.2876088.
Cícero A. Silva, Gibeon S. Aquino, Sávio R. M. Melo, Dannylo J. B. Egídio, "A Fog Computing-Based Architecture for Medical Records Management", Wireless Communications and Mobile Computing, vol. 2019, Article ID 1968960, 16 pages, 2019. https://doi.org/10.1155/2019/1968960
Mishra, S. & Prakash, M.,”Study of fuzzy logic in medical data analytics”, 2018, International Journal of Pure and Applied Mathematics. 119. 16321-16342.
Dragan Simić, IlijaKovačević, Vasa Svirčević, Svetlana Simić, “50 years of fuzzy set theory and models for supplier assessment and selection: A literature review”, 2017, Journal of Applied Logic, Volume 24, Part A, Pages 85-96, ISSN 1570-8683, https://doi.org/10.1016/j.jal.2016.11.016.
WU, Hangyao; XU, Zeshui, “Fuzzy Logic in Decision Support: Methods, Applications and Future Trends”, 2020 INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 16, n. 1, sep. 2020. ISSN 1841-9844
Yuan, Bingchuan& Herbert, John, “Fuzzy CARA - A Fuzzy-Based Context Reasoning System For Pervasive Healthcare”, 2012, Procedia Computer Science. 10. 357–365. 10.1016/j.procs.2012.06.047.
Jemal, Hanen&Kechaou, Zied& Ben Ayed, Mounir, “Multi-agent based intuitionistic fuzzy logic healthcare decision support system”, 2019, Journal of Intelligent & Fuzzy Systems. 37. 1-16. 10.3233/JIFS-182926.
ColellaYlenia, De Lauri Chiara, Improta Giovanni, Rossano Lucia, Vecchione Donatella, SpinosaTiziana, Giordano Vincenzo, VerdolivaCiro, SantiniStefania, “A Clinical Decision Support System based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients”. Mathematical Biosciences and Engineering, 2021, 18(3): 2654-2674. doi: 10.3934/mbe.2021135
Mrozek D., Milik M., Małysiak-Mrozek B., Tokarz K., Duszenko A., Kozielski S., “Fuzzy Intelligence in Monitoring Older Adults with Wearables”, 2020 In: Krzhizhanovskaya V.V. et al. (eds) Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in Computer Science, vol 12141. Springer, Cham. https://doi.org/10.1007/978-3-030-50426-7_22
Kashif Hameed, Imran SarwarBajwa, ShabanaRamzan, Waheed Anwar, Akmal Khan, "An Intelligent IoT Based Healthcare System Using Fuzzy Neural Networks", Scientific Programming, vol. 2020, Article ID 8836927, 15 pages, 2020. https://doi.org/10.1155/2020/8836927
SyedaBinish Zahra, Talmeez Hussain, Ayesha Atta, M. Saleem Khan, “Human Blood Pressure and Body Temp Analysis Using Fuzzy Logic Control System”, IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.12, December 2017
Al-Dmour, Jumanah&Sagahyroon, Assim& Al-Ali, AR &Abusnana, Salah. “A fuzzy logic–based warning system for patients classification”, 2017, Health Informatics Journal. 25. 146045821773567. 10.1177/1460458217735674.
Hussain, Aamir, Wenbi Rao, X. Zheng, Hongyang Wang and Lopes da Silva Aristides. “Personal Home Healthcare System for the Cardiac Patient of Smart City Using Fuzzy Logic.” Journal of Advances in Information Technology 7 (2016): 58-64.
Neloy, Asif &Oshman, Muhammad & Islam, Md & Hossain, Md &Zahir, Zunayeed-Bin, “Content-Based Health Recommender System for ICU Patient”, 2019, 10.1007/978-3-030-33709-4_20.
Muneeb Ahmed Anwar, Muazzam A. Khan, Naveed Iqbal, Ahmad Hassan, BalawalShabir, “Patient Health Monitoring in ICU using Internet of Things”, EasyChair Preprint, January 29, 2020
Vani K S and Rajesh RayappaNeeralagi, “IoT based Health Monitoring using Fuzzy logic”, International Journal of Computational Intelligence Research ISSN 0973-1873 Volume 13, Number 10 (2017), pp. 2419-2429
Rao, N., Kumar, A., &Abbasi, T. A., “Mobile Health Monitoring System using Fuzzy Logic”. 2016, INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 15(7), 6947–6949. https://doi.org/10.24297/ijct.v15i7.1534
Thilagavathy A, Meenakshi S, Vijayabhaskar V, Babu MD, Kumari S, Gunavathie MA, “An Efficient Health Monitoring Method Using Fuzzy Inference System via Cloud”, 2021, Indian Journal of Science and Technology 14(25): 2145-2151.
D. Surekha, R. K Aanchana, E. Leena, R. Pooja, “Implementation of Event Triggering Algorithm for Remote Patient Monitoring using Fog Computing”, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) ECLECTIC – 2020 (Volume 8 – Issue 07)
Humadi, Aqeel&Khalaf, Alaa, “Online Real Time Fuzzy Inference System Based Human Health Monitoring and Medical Decision Making”, 2017, International Journal of Computer Science and Information Security. 15. 197. 10.2139/ssrn.3027091.
Shatnawi, Maad&Shatnawi, Anas&AlShara, Zakarea&Husari, Ghaith, “Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis”, 2021, International Journal of Advanced Computer Science and Applications. 12. 10.14569/IJACSA.2021.0120457.
Gadekallu, Thippa&Khare, Neelu, “An Efficient System for Heart Disease Prediction using Hybrid OFBAT with Rule-Based Fuzzy Logic Model”, 2016, Journal of Circuits, Systems and Computers. 26. 1750061. 10.1142/S021812661750061X.
Dumitrescu, Catalin, PetricaCiotirnae, and Constantin Vizitiu, "Fuzzy Logic for Intelligent Control System Using Soft Computing Applications", 2021, Sensors 21, no. 8: 2617. https://doi.org/10.3390/s21082617
Jayalakshmi, M & Garg, Lalit&Maharajan, K &Kaliappan, Jayakumar& Srinivasan, Kathiravan& Bashir, Ali & Ramesh, K., “Fuzzy Logic-Based Health Monitoring System for COVID'19 Patients”, 2021, Cmc -Tech Science Press-. 67. 2431-2447. 10.32604/cmc.2021.015352.
Quasim MT, Shaikh A, Shuaib M, et al. “Smart Healthcare Management Evaluation using Fuzzy Decision Making Method”, Research Square; 2021. DOI: 10.21203/rs.3.rs-424702/v1.
Medjahed, Hamid &Istrate, Dan &Boudy, Jérôme&Baldinger, Jean-Louis &Bougueroua, Lamine&Dhouib, Mohamed &Dorizzi, Bernadette, “A Fuzzy Logic Approach for Remote Healthcare Monitoring by Learning and Recognizing Human Activities of Daily Living”, 2012, https://doi.org/10.5772/36420.
Alfaras, M., Soriano, M.C., & Ortin, S. (2019). A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection. Frontiers in Physics.
Mustaqeem, A., Anwar, S.M., & Majid, M. (2018). Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants. Computational and Mathematical Methods in Medicine, 2018.
Alarsan, F.I., &Younes, M. (2019). Analysis and classification of heart diseases using heartbeat features and machine learning algorithms. Journal of Big Data, 6, 1-15.
Che, C., Zhang, P., Zhu, M., Qu, Y., &Jin, B. (2021). Constrained transformer network for ECG signal processing and arrhythmia classification. BMC Medical Informatics and Decision Making, 21.
Feeny, A.K., Chung, M.K., Madabhushi, A., Attia, Z.I., Čikeš, M., Firouznia, M., Friedman, P.A., Kalscheur, M.M., Kapa, S., Narayan, S.M., Noseworthy, P.A., Passman, R.S., Perez, M.V., Peters, N.S., Piccini, J.P., Tarakji, K.G., Thomas, S.A., Trayanova, N.A., Turakhia, M.P., & Wang, P. (2020). Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. Circulation: Arrhythmia and Electrophysiology, 13, e007952.
Pandey, S.K., &Janghel, R.R. (2019). Automatic detection of arrhythmia from imbalanced ECG database using CNN model with SMOTE. Australasian Physical & Engineering Sciences in Medicine, 42, 1129 - 1139.
Samir AbdElMoneem, S., Hanafy Said, H., & Anwar Saad, A. (2020). Arrhythmia Disease Classification and Mobile Based System Design.
Topic, A., & Russo, M. (2021). Emotion recognition based on EEG feature maps through deep learning network. Engineering Science and Technology, an International Journal.
Doma, V., &Pirouz, M. (2020). A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals. Journal of Big Data, 7, 1-21.
Li, Y., Zheng, W., Cui, Z., Zhang, T., &Zong, Y. (2018). A Novel Neural Network Model based on Cerebral Hemispheric Asymmetry for EEG Emotion Recognition. IJCAI.
Bilucaglia, M., Duma, G.M., Mento, G., Semenzato, L., & Tressoldi, P.E. (2020). Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity. F1000Research, 9, 173.
Alhalaseh, R., &Alasasfeh, S. (2020). Machine-Learning-Based Emotion Recognition System Using EEG Signals. Comput., 9, 95.
Khan, A.N., Ihalage, A.A., Ma, Y., Liu, B., Liu, Y., &Hao, Y. (2021). Deep learning framework for subject-independent emotion detection using wireless signals. PloS one, 16 2, e0242946.
Liu, J., Wu, G., Luo, Y., Qiu, S., Yang, S., Li, W., & Bi, Y. (2020). EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder. Frontiers in Systems Neuroscience, 14.
Gannouni, S., Aledaily, A., Belwafi, K., &Aboalsamh, H.A. (2021). Emotion detection using electroencephalography signals and a zero-time windowing-based epoch estimation and relevant electrode identification. Scientific Reports, 11.
Kusumaningrum, T.D., Faqih, A., & Kusumoputro, B. (2020). Emotion Recognition Based on DEAP Database using EEG Time-Frequency Features and Machine Learning Methods.
Pan, B., & Zheng, W. (2021). Emotion Recognition Based on EEG Using Generative Adversarial Nets and Convolutional Neural Network. Computational and Mathematical Methods in Medicine, 2021.
Alhagry, S., Fahmy, A.A., & El-Khoribi, R.A. (2017). Emotion Recognition based on EEG using LSTM Recurrent Neural Network. International Journal of Advanced Computer Science and Applications.
Bazgir, O., Mohammadi, Z., & Habibi, S.A. (2018). Emotion Recognition with Machine Learning Using EEG Signals. 2018 25th National and 3rd International Iranian Conference on Biomedical Engineering (ICBME), 1-5.
Alhalaseh, R., &Alasasfeh, S. (2020). Machine-Learning-Based Emotion Recognition System Using EEG Signals. Comput., 9, 95.
Zheng, W., & Lu, B. (2017). A Multimodal Approach to Estimating Vigilance Using EEG and Forehead EOG. Journal of neural engineering, 14 2, 026017.
Vishwa, Abhinav&Lal, Mohit& Dixit, Sharad&Varadwaj, Pritish. (2022). Clasification Of Arrhythmic ECG Data Using Machine Learning Techniques. International Jorunal of Interactive Multimedia and Artificial Intelligence. 1. 67-70. 10.9781/ijimai.2022.1411.
Vaishali M. Joshi, Rajesh B.Ghongade, “Emotion Detection with Single Channel EEG Signal using Deep Learning Algorithm”, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878 (Online), Volume-8 Issue-6, March 2020
Kanna, D. ., & Muda, I. . (2021). Hybrid Stacked LSTM Based Classification in Prediction of Weather Forecasting Using Deep Learning. Research Journal of Computer Systems and Engineering, 2(1), 46:51. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/22
Anand, R., Khan, B., Nassa, V. K., Pandey, D., Dhabliya, D., Pandey, B. K., & Dadheech, P. (2023). Hybrid convolutional neural network (CNN) for kennedy space center hyperspectral image. Aerospace Systems, 6(1), 71-78. doi:10.1007/s42401-022-00168-4
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