Novel Internet of Things Based Disease Diagnosis Framework for Smart Healthcare Schemes using Combined Optimized Artificial Intelligence Approach
Keywords:
Artificial intelligence, Healthcare, performance measure, Internet of Things, Feature extraction, Classification, Sensors and Cloud storageAbstract
The Internet of Things (IoT) and Artificial Intelligence (AI) technologies have gotten a lot of attention in recent years as a means to alleviate the load on healthcare systems caused by an aging population and a surge in severe diseases. However, the present diagnosis systems have several flaws, such as a long computational time and reduced prediction accuracy. Due to this reason, this article proposes a new improved IoT-based hybrid AI model for diagnosing heart, diabetes, and kidney diseases. Here, the UCI Repository dataset is used in this research. The implementation of this work is executed in MATLAB software. Also, the exact features from the dataset for accurate classification are obtained by the proposed Hierarchy mapping-based heap optimizer (HM-HO) method. Furthermore, the Shamble Shepherd Optimizer-based Evolving fuzzy neural network (SSO-EuFNN) algorithm is proposed for accurate disease diagnosis classification. Furthermore, 99.91% precision for heart disease, 99.88% accuracy for diabetes, and 99.82% accuracy for kidney disease were attained in the indicated disease diagnostic simulation findings. Thus the simulation results achieved from the proposed model are compared with the conventional methods in terms of various performance measures. As a result, the proposed strategies successfully reduce the death rate by lowering the complexity of disease diagnosis.
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References
Care, Diabetes. "Standards of medical care in diabetes 2019." Diabetes Care 42.Suppl 1 (2019): S124-138.
Tian, Shuo, et al. "Smart healthcare: making medical care more intelligent." Global Health Journal 3.3 (2019): 62-65.
Biener, Adam, John Cawley, and Chad Meyerhoefer. "The impact of obesity on medical care costs and labor market outcomes in the US." Clinical chemistry 64.1 (2018): 108-117.
Zhuang, Jing, Jianli Sun, and Guoliang Yuan. "Arrhythmia diagnosis of young martial arts athletes based on deep learning for smart medical care." Neural Computing and Applications (2021): 1-12.
Ju, Shumei, Yanfang Sun, and Yingjie Su. "Internet of things smart medical system and nursing intervention of glucocorticoid drug use." Microprocessors and Microsystems 83 (2021): 104008.
Cheng, Yanming, et al. "Research on the Smart Medical System Based on NB-IoT Technology." Mobile Information Systems 2021 (2021).
Zhong, Yi, ZhiHai Xu, and Lei Cao. "Intelligent IoT-based telemedicine systems implement for smart medical treatment." Personal and Ubiquitous Computing (2021): 1-11.
Shao, Chenghui, et al. "Smart home healthcare system based on middleware and counter neural network." Journal of Medical Imaging and Health Informatics 10.5 (2020): 1105-1112.
Tholkapiyan, M. ., Ramadass, S. ., Seetha, J. ., Ravuri, A. ., Vidyullatha, P. ., Shankar S., S . . . and Gore, S. . . (2023) “Examining the Impacts of Climate Variability on Agricultural Phenology: A Comprehensive Approach Integrating Geoinformatics, Satellite Agrometeorology, and Artificial Intelligence”, International Journal of Intelligent Systems and Applications in Engineering, 11(6s), pp. 592–598. Available at: https://ijisae.org/index.php/IJISAE/article/view/2891.
Brown, John, JurajCug, and JurajKolencik. "Internet of things-based smart healthcare systems: real-time patient-generated medical data from networked wearable devices." American Journal of Medical Research 7.1 (2020): 21-26.
Tuli, Shreshth, et al. "Next generation technologies for smart healthcare: challenges, vision, model, trends and future directions." Internet Technology Letters 3.2 (2020): e145.
Puri, Vikram, AmanKataria, and Vishal Sharma. "Artificial intelligence‐powered decentralized framework for Internet of Things in Healthcare 4.0." Transactions on Emerging Telecommunications Technologies (2021): e4245.
Wright, Susan, and Milos Birtus. "Real-time medical data analytics in Internet of Things-based smart healthcare systems." American Journal of Medical Research 7.1 (2020): 61-66.
Sundaravadivel, Prabha, et al. "Everything you wanted to know about smart health care: Evaluating the different technologies and components of the internet of things for better health." IEEE Consumer Electronics Magazine 7.1 (2017): 18-28.
Ghazal, Taher M. "Internet of Things with Artificial Intelligence for Health Care Security." Arabian Journal for Science and Engineering (2021): 1-12.
Guo, Kun, et al. "Artificial intelligence-based semantic internet of things in a user-centric smart city." Sensors 18.5 (2018): 1341.
Bargh, Matthew. "Digital health software and sensors: internet of things-based healthcare services, wearable medical devices, and real-time data analytics." American Journal of Medical Research 6.2 (2019): 61-66.
Chen, Min, et al. "Edge cognitive computing based smart healthcare system." Future Generation Computer Systems 86 (2018): 403-411.
Rajan, J. Pandia, et al. "Fog computing employed computer aided cancer classification system using deep neural network in internet of things based healthcare system." Journal of medical systems 44.2 (2020): 1-10.
Yin, Hongxu, and Niraj K. Jha. "A health decision support system for disease diagnosis based on wearable medical sensors and machine learning ensembles." IEEE Transactions on Multi-Scale Computing Systems 3.4 (2017): 228-241.
Nilashi, Mehrbakhsh, et al. "Coronary heart disease diagnosis through self-organizing map and fuzzy support vector machine with incremental updates." International Journal of Fuzzy Systems 22.4 (2020): 1376-1388.
Dulhare, Uma N. "Prediction system for heart disease using Naive Bayes and particle swarm optimization." Biomedical Research 29.12 (2018): 2646-2649.
Maleki, Negar, Yasser Zeinali, and SeyedTaghiAkhavanNiaki. "A k-NN method for lung cancer prognosis with the use of a genetic algorithm for feature selection." Expert Systems with Applications 164 (2021): 113981.
Abbas, Hasan T., et al. "Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test." Plos one 14.12 (2019): e0219636.
Al-Behadili, HayderNaserKhraibet, and Ku Ruhana Ku-Mahamud. "Fuzzy unordered rule using greedy hill climbing feature selection method: An application to diabetes classification." Journal of Information and Communication Technology 20.3 (2021): 391-422.
Xu, Hongxia, Yonghui Kong, and Shaofeng Tan. "Predictive modeling of diabetic kidney disease using random forest algorithm along with features selection." Proceedings of the 2020 International Symposium on Artificial Intelligence in Medical Sciences. 2020.
Chowdhury, Nakib Hayat, et al. "Performance analysis of Conventional machine learning algorithms for identification of chronic kidney disease in type 1 diabetes mellitus patients." Diagnostics 11.12 (2021): 2267.
Padmanaban, K. Anantha, and G. Parthiban. "Applying machine learning techniques for predicting the risk of chronic kidney disease." Indian Journal of Science and Technology 9.29 (2016): 1-6.
Nichachotesalid, Pirin, et al. "A Retrospective Combination Health Outcomes Study among Diabetic Patients with Chronic Kidney Disease: A Multi-Level Study." Trends in Sciences 19.8 (2022): 3223-3223.
Dange, B. J. ., Mishra, P. K. ., Metre, K. V. ., Gore, S. ., Kurkute, S. L. ., Khodke, H. E. . and Gore, S. . (2023) “Grape Vision: A CNN-Based System for Yield Component Analysis of Grape Clusters”, International Journal of Intelligent Systems and Applications in Engineering, 11(9s), pp. 239–244. Available at: https://ijisae.org/index.php/IJISAE/article/view/3113.
Komal, Kumar N., R. Lakshmi Tulasi, and D. Vigneswari. "An ensemble multi-model technique for predicting chronic kidney disease." International Journal of Electrical and Computer Engineering 9.2 (2019): 1321.
Khan, Mohammad Ayoub. "An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier." IEEE Access 8 (2020): 34717-34727.
Lakshmanaprabu, S. K., et al. "Online clinical decision support system using optimal deep neural networks." Applied Soft Computing 81 (2019): 105487.
Kaur, Pavleen, Ravinder Kumar, and Munish Kumar. "A healthcare monitoring system using random forest and internet of things (IoT)." Multimedia Tools and Applications 78.14 (2019): 19905-19916.
Gore, S. ., Dutt, I. ., Dahake, R. P. ., Khodke, H. E. ., Kurkute, S. L. ., Dange, B. J. . and Gore, S. . (2023) “Innovations in Smart City Water Supply Systems ”, International Journal of Intelligent Systems and Applications in Engineering, 11(9s), pp. 277–281. Available at: https://ijisae.org/index.php/IJISAE/article/view/3118.
Liu, Zhuo, et al. "Deep reinforcement learning with its application for lung cancer detection in medical Internet of Things." Future Generation Computer Systems 97 (2019): 1-9.
Kishor, Amit, and Chinmay Chakraborty. "Artificial intelligence and internet of things based healthcare 4.0 monitoring system." Wireless Personal Communications (2021): 1-17.
Basheer, Shakila, Ala Saleh Alluhaidan, and Maryam AyshaBivi. "Real-time monitoring system for early prediction of heart disease using Internet of Things." Soft Computing (2021): 1-14.
Haq, Amin Ul, et al. "Intelligent machine learning approach for effective recognition of diabetes in E-healthcare using clinical data." Sensors 20.9 (2020): 2649.
Mansour, Romany Fouad, et al. "Artificial Intelligence and Internet of Things Enabled Disease Diagnosis Model for Smart Healthcare Systems." IEEE Access 9 (2021): 45137-45146.
Vijayashree, J., and H. Parveen Sultana. "Heart disease classification using hybridized Ruzzo-Tompa memetic based deep trained Neocognitron neural network." Health and Technology 10.1 (2020): 207-216.
Chen, Guozhen, et al. "Prediction of chronic kidney disease using adaptive hybridized deep convolutional neural network on the internet of medical things platform." IEEE Access 8 (2020): 100497-100508.
Datar, D. ., & Khobragade, R. N. . (2023). Mental State Prediction Using Machine Learning and EEG Signal. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4), 07–12. https://doi.org/10.17762/ijritcc.v11i4.6374
Sharma, M. K. (2021). An Automated Ensemble-Based Classification Model for The Early Diagnosis of The Cancer Using a Machine Learning Approach. Machine Learning Applications in Engineering Education and Management, 1(1), 01–06. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/1
Dhabliya, D., & Parvez, A. (2019). Protocol and its benefits for secure shell. International ournal of Control and Automation, 12(6 Special Issue), 19-23. Retrieved from www.scopus.com
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