Application of IoT Framework for Prediction of Heart Disease using Machine Learning
Article Sidebar
Main Article Content
Abstract
Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft computing models can be helpful for remedy of various heart diseases. The Food and Drug Administration (FDA) employs several particularly creative thoughts to get their capsules to the client. Artificial Neural Community offers a first-rate chance to deal with heart diseases with advance IoT and cloud applications.
Article Details
References
N. Alhussien and T. A. Gulliver, "Optimal Resource Allocation in Cellular Networks With H2H/M2M Coexistence," in IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 12951-12962, Nov. 2020, doi: 10.1109/TVT.2020.3016239.
O. Juki?, I. He?i and E. Cirikovi?, "IoT cloud-based services in network management solutions," 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), Opatija, Croatia, 2020, pp. 419-424, doi: 10.23919/MIPRO48935.2020.9245117.
C. Botta, L. Pierangelini and L. Vollero, "IoT Gateways for Industrial and Medical Applications: Architecture and Performance Assessment," 2020 IEEE International Workshop on Metrology for Industry 4.0 &IoT, Roma, Italy, 2020, pp. 596-599, doi: 10.1109/MetroInd4.0IoT48571.2020.9138268.
Mukhopadhyay, S.C.; Suryadevara, N.K.; Nag, A. Wearable Sensors for Healthcare: Fabrication to Application. Sensors 2022,22, 5137. [CrossRef]
F. A. Dharejo et al., "FuzzyAct: A Fuzzy-Based Framework for Temporal Activity Recognition in IoT Applications Using RNN and 3D-DWT," in IEEE Transactions on Fuzzy Systems, vol. 30, no. 11, pp. 4578-4592, Nov. 2022, doi: 10.1109/TFUZZ.2022.3152106.
Azlina Abdullah, Ismail Musirin, Muhammad Murtadha Othman, Siti Rafidah Abdul Rahim, A.V. Sentilkumar. (2023). Multi-DGPV Planning Using Artificial Intelligence. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 377–391. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2677
A. Rabay'a, E. Schleicher and K. Graffi, "Fog Computing with P2P: Enhancing Fog Computing Bandwidth for IoT Scenarios," 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Atlanta, GA, USA, 2019, pp. 82-89, doi: 10.1109/iThings/GreenCom/CPSCom/SmartData.2019.00036
Ren, J.; Li, J.; Liu, H.; Qin, T. Task offloading strategy with emergency handling and blockchain security in SDN-empowered andfog-assisted healthcare IoT. Tsinghua Sci. Technol. 2021, 27, 760–776. [CrossRef]
Saminathan, S.; Geetha, K. Real-time health care monitoring system using IoT. Int. J. Eng. Technol. 2018, 7, 484–488
TahiaTazin et al. “Stroke Disease Detection and Prediction Using Robust Learning Approaches”. In: Journal ofhealthcare engineering 2021 (2021), p. 7633381. ISSN:2040-2295. DOI: 10.1155/2021/7633381. URL: https://europepmc.org/articles/PMC8641997
Dritsas, E., Alexiou, S., Moustakas, K.: Cardiovascular disease risk prediction with supervised machine learning techniques. In: ICT4AWE, pp. 315–321 (2022)
Al-Ansi, A. M. . (2021). Applying Information Technology-Based Knowledge Management (KM) Simulation in the Airline Industry . International Journal of New Practices in Management and Engineering, 10(02), 05–09. https://doi.org/10.17762/ijnpme.v10i02.131
Harshitha K V et al. “Stock Prediction using Machine Learning Algorithm”. In: International Journal of Innovative Research in Engineering and Management, ISSN: 2350-0557. Vol. 8, Issue 4, July 2021.
Mainali S, Darsie ME, Smetana KS. Machine learning inaction: Stroke diagnosis and outcome prediction. Front Neurol2021;12:734345
A. Asokan, B. Priyanka, S. Thenmozhi and K. Sumathi, "IoT Based Healthcare System for Emergency Medical Services," 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2021, pp. 480-482, doi: 10.1109/ICECA52323.2021.9675896
M. Panwar, A. Gautam, D. Biswas and A. Acharyya, "PP-Net: A Deep Learning Framework for PPG-Based Blood Pressure and Heart Rate Estimation," in IEEE Sensors Journal, vol. 20, no. 17, pp. 10000-10011, 1 Sept.1, 2020, doi: 10.1109/JSEN.2020.2990864
Jayasingh, S. K., Gountia, D., Samal, N., & Chinara, P. K. (2021). A novel approach for data classification using neural network. IETE Journal of Research, 1-7.
Jayasingh, S.K., Mantri, J.K., Pradhan, S. (2022). Smart Weather Prediction Using Machine Learning. In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 431. Springer, Singapore. https://doi.org/10.1007/978-981-19-0901-6_50.
Jayasingh, S.K., Mantri, J.K., Pradhan, S. (2021). Weather Prediction Using Hybrid Soft Computing Models. In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 185. Springer, Singapore. https://doi.org/10.1007/978-981-33-6081-5_4.
Christopher Davies, Matthew Martinez, Catalina Fernández, Ana Flores, Anders Pedersen. Predicting Dropout Risk in Higher Education Using Machine Learning. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/170
Prusty, S.R., Sainath, B., Jayasingh, S.K., Mantri, J.K. (2022). SMS Fraud Detection Using Machine Learning. In: Udgata, S.K., Sethi, S., Gao, XZ. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 431. Springer, Singapore. https://doi.org/10.1007/978-981-19-0901-6_52.
Jayasingh, S. K., Mantri, J. K., & Gahan, P. (2016). Comparison between J48 Decision Tree, SVM and MLP in Weather Forecasting. International Journal of Computer Science and Engineering, 3(11), 42-47.
Mantri, J. K., & Jayasingh, S. K. (2019). Soft Computing Techniques for Weather Change Predictions in Delhi. International Journal of Recent Technology and Engineering, 8(4), 793-800.
A. K. Swain, A. Swetapadma, J. K. Rout and B. K. Balabantaray, "A Non-small Cell Lung Cancer Detection Technique Using PET/ CT Images," 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, 2023, pp. 1-4, doi: 10.1109/ICECCT56650.2023.10179811.
Swain, A. K., Swetapadma, A., Rout, J. K., & Balabantaray, B. K. (2023). A hybrid learning method for distinguishing lung adenocarcinoma and squamous cell carcinoma. Data Technologies and Applications.