Heart And Diabetes Disease Detection Using Adam Optimization Algorithm (Deep Learning)
Keywords:
Artificial Intelligence, Data set, Adam Optimization Algorithm, Stochastic Gradient Descent Algorithm, Prediction, Deep learning, Machine learning.Abstract
The application of artificial intelligence system to medical research has the prospective to move forward highly advanced in e-health systems. Healthcare prediction is one of the significant factors in saving human lives in recent years. Heart and diabetes disease is one of the key contributors to human death. In the domain of healthcare systems, there is a rapid development of intelligent systems for analyzing the complicated relationships among huge amount of data and transforming into real information for prediction purpose. With the various techniques and methods developed the heart and diabetes detection systems. Pattern identification from images has become very easier by using machine learning techniques. In this paper proposing the deep learning based Adam optimization algorithm for effective image classification in the precise manner for heart and diabetes diseases detection. The proposed mode evaluated based on the 14 attributes with 2000 patients. Proposed method considers the performance metric as accuracy and precision, recall and F1 measure. Our proposed model achieved validation accuracy as a 92.81% when compare to stochastic gradient descent algorithm.
References
- Aceto, G.; Persico, V.; Pescapé, A. The role of Information and Communication Technologies in healthcare: Taxonomies, perspectives, and challenges. J. Netw. Comput. Appl. 2018, 107, 125–154.
- Aarathi, S.; Vasundra, S. Impact of healthcare predictions with big data analytics and cognitive computing techniques. Int. J. Recent Technol. Eng. 2019, 8, 4757–4762.
- Lhotska, L. Application of industry 4.0 concept to health care. Stud. Health Technol. Inform. 2020, 273, 23–37.
- Oliver, N.; Arnesh, T.; Tak, I. Smart hospital services: Health 4.0 and opportunity for developing economies. In Proceedings of the Towards the Digital World and Industry X.0—Proceedings of the 29th International Conference of the International Association for Management of Technology, IAMOT 2020, Cairo, Egypt, 13–17 September 2020; pp. 345–361.
- Yin, Y.; Zeng, Y.; Chen, X.; Fan, Y. The internet of things in healthcare: An overview. J. Ind. Inf. Integr. 2016, 1, 3–13.
- Fatt, Q.K.; Ramadas, A. The Usefulness and Challenges of Big Data in Healthcare. J. Healthc. Commun. 2018, 3, 1–4.
- Lee, J. Industrial AI: Applications with Sustainable Performance; Springer: Berlin/Heidelberg, Germany, 2020.
- Tsikala Vafea, M.; Atalla, E.; Georgakas, J.; Shehadeh, F.; Mylona, E.K.; Kalligeros, M.; Mylonakis, E. Emerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19. Cell. Mol. Bioeng. 2020, 13, 249–257.
- Bates, D.W.; Saria, S.; Ohno-Machado, L.; Shah, A.; Escobar, G. Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Aff. 2014, 33, 1123–1131.
- Lee, I. Big data: Dimensions, evolution, impacts, and challenges. Bus. Horiz. 2017, 60, 293–303.
- Chinnaswamy, A.; Papa, A.; Dezi, L.; Mattiacci, A. Big data visualisation, geographic information systems and decision making in healthcare management. Manag. Decis. 2019, 57, 1937–1959.
- Sumarsono; Anshari, M.; Almunawar, M.N. Big Data in Healthcare for Personalization Customization of Healthcare Services. In Proceedings of the 2019 International Conference on Information Management and Technology (ICIMTech), Jakarta/Bali, Indonesia, 19–20 August 2019; Volume 1, pp. 73–77.
- Chen, H.C.; Chiang, R.H. Business intelligence and analytics: From big data to big impact. MIS Q. 2012, 36, 1165–1188.
- Tran, T.Q.B.; du Toit, C.; Padmanabhan, S. Artificial intelligence in healthcare-the road to precision medicine. J. Hosp. Manag. Health Policy 2021, 5, 29.
- Weaver, C.A.; Ball, M.J.; Kim, G.R.; Kiel, J.M. Healthcare information management systems: Cases, strategies, and solutions: Fourth edition. In Healthcare Information Management Systems: Cases, Strategies, and Solutions, 4th ed.; Springer International Publishing: Cham, Switzerland, 2016; pp. 1–600. ISBN 9783319207650.
- Firouzi, F.; Rahmani, A.M.; Mankodiya, K.; Badaroglu, M.; Merrett, G.V.; Wong, P.; Farahani, B. Internet-of-Things and big data for smarter healthcare: From device to architecture, applications and analytics. Future Gener. Comput. Syst. 2018, 78, 583–586.
- Zhou, C.; Su, F.; Pei, T.; Zhang, A.; Du, Y.; Luo, B.; Cao, Z.; Wang, J.; Yuan, W.; Zhu, Y.; et al. COVID-19: Challenges to GIS with Big Data. Geogr. Sustain. 2020, 1, 77–87.
- Shokoohi, M.; Osooli, M.; Stranges, S. COVID-19 Pandemic: What Can the West Learn From the East? Int. J. Health Policy Manag. 2020, 9, 436–438.
- Wang, Y.; Kung, L.A.; Byrd, T.A. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Chang. 2018, 126, 3–13.
- Nalluri, S.; Sasikala, R. An insight into application of big data analytics in healthcare. Int. J. Data Min. Model. Manag. 2020, 12, 87–117.
- Patan, R.; Kallam, S.; Gandomi, A.H.; Hanne, T.; Ramachandran, M. Gaussian relevance vector MapReduce-based annealed Glowworm optimization for big medical data scheduling. J. Oper. Res. Soc. 2021, 1–12.
- Raghupathi, W.; Raghupathi, V. Big data analytics in healthcare: Promise and potential. Health Inf. Sci. Syst. 2014, 2, 3.
- Wang, Y.; Hajli, N. Exploring the path to big data analytics success in healthcare. J. Bus. Res. 2017, 70, 287–299.
- Zolbanin, H.M.; Delen, D.; Sharma, S.K. The strategic value of big data analytics in health care policy-making. Int. J. E-Bus. Res. 2018, 14, 20–33.
- Basile, L.J.; Carbonara, N.; Pellegrino, R.; Panniello, U. Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making. Technovation 2022, 102482.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRCSEIT
This work is licensed under a Creative Commons Attribution 4.0 International License.