Use of Artificial Intelligence for Healthcare Purposes: A Structured Review
DOI:
https://doi.org/10.26438/ijcse/v10i8.2327Keywords:
Machine Leaning, Healthcare, Artifical intelligence, Data miningAbstract
Artificial intelligence, which is widely defined as a machine's ability to mimic intelligent human behaviour, includes the subfield of machine learning. Artificial intelligence systems are employed to carry out difficult jobs in a manner a kin to how people solve problems. Data mining is a method of automatically discovering information from massive databases or data sets. Data mining is a technique for locating the information that is concealed in a large, challenging-to-access database using only standard queries. Additionally, machine learning demonstrates its close ties to data mining. Making software that teaches computers to get smarter as they encounter new data is the core objective of machine learning. Prediction systems, image identification, Speech recognition, medical diagnosis and other applications employ machine learning. Numerous areas, including research, engineering, health care, and business, produce and a vast volumes of data every day. In this study, a few data mining approaches, algorithms, and applications are discussed. Also, application of artificial intelligence in the field of healthcare industry such as Oral Pre-Cancerous Lesions and Oral Cancer Detection, Neurodegenerative disorders, Medical Disorders, Drug Discovery are discussed.
References
[1] Solanki, H., 2013, “ Comparative Study of Data Mining Tools and Analysis with Unified Data Mining Theory” in International Journal of Computer Applications, 75(16), pp.23-28, 2013.
[2] S. L. Nalawade ,Dr. R.V. Kulkarni,“Application of Data Mining in Health Care” in International Journal of Science and Research (IJSR), 5(4), pp.262-268, 2016.
[3] Ms.Dhwani Sondhi "Application of Data Mining in Census Data Analysis using Weka", International Journal of Engineering Trends and Technology (IJETT), V52(3),157-161 October 2017. ISSN:2231-53817.
[4] Saima Anwar Lashari, Rosziati Ibrahim, Norhalina Senan, N. S. A. M. Taujuddin,”Application of Data Mining Techniques for Medical Data Classification: A Review”in MATEC Web of Conferences 150, 06003, 2018.
[5] D.Usha Rani, “A Survey on Data Mining Tools and Techniques in Medical Field” in International Journal of Advanced Networking & Applications (IJANA), Vol.08, Issue: 05 Pages: 51-54, 2017.
[6] Manpreet Kaura, Shivani Kanga, “Market Basket Analysis: Identify the changing trends of market data using association rule mining” in International Conference on Computational Modeling and Security (CMS 2016), Procedia Computer Science 85, 78 – 85, 2016.
[7] M. Dhanabhakyam, Dr. M. Punithavalli, “A Survey on Data Mining Algorithm for Market Basket Analysis”in Global Journal of Computer Science and Technology Vol.11, Issue 11 Version 1.0 July 2011.
[8] Solanki, H., 2013,“Comparative Study of Data Mining Tools and Analysis with Unified Data Mining Theory” in International Journal of Computer Applications, 75(16), pp.23-28, 2013.
[9] Ashish Dutti, Maizatul Akmar Ismaili, Tutut Herawan, “ A Systematic Review on Educational Data Mining”in IEEE ACCESS, Vol.5, pp 15991-16005, 2017.
[10] Stefano Lonardi, Jake Chen, ?Data Mining in Bioinformatics: Selected Papers from BIOKDD in IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol.7, no. 2, April-June 2010.
[11] V.K. Jha, R.K. Singh, “Application of Data Mining in Manufacturing Industry” in International Journal of Information Sciences and Application. ISSN 0974- 2255 Vol.3, Number 2, pp. 59-64, 2011.
[12] Uddin, Osemengbe O., P. S. O. Uddin, “Data Mining: An Active Solution for Crime Investigation in IJCST Vol.5, SPl - 1, Jan - Mar 2014.
[13] Kumar, Y., Koul, A., Singla, R. and Ijaz, M., “Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda” in Journal of Ambient Intelligence and Humanized Computing.
[14] Han Y, Han Z, Wu J, Yu Y, Gao S, Hua D, Yang A, “Artificial intelligence recommendation system of cancer rehabilitation scheme based on IoT technology” IEEE Access. 2020;8.44924–44935.
[15] Guram, Harsimran and Sharma, Ashok, Comparing the Performance of Various Machine Learning Approaches in Identifying Different Classes of Dementia (April 25, 2021). Proceedings of the International Conference on Innovative Computing & Communication (ICICC) 2021.
[16] Guram, Harsimran and Ashok, “improved dementia images detection and classification using transfer learning base convolution mapping with attention layer and xgboost classifier in Turkish Journal of Computer and Mathematics Education Vol.12No.6, 217-224, 2021
[17] Harsimran Guram Ashok Sharma,” Identifying the Rules of Transition from AD to Dementia and MCI Using Machine Learning in International Journal of Control and Automation, Vol.13 issue 4 Pp-718-729
[18] Guram, H. and Sharma, A., 2021. Patch base segmentation for classification of dementia disorder with optimize feature weight and random forest-based approach. Materials Today: Proceedings, [online] Available [Accessed 11 September 2022]
[19] Sharma, A., Singh, P. and Dhar, G., 2021. Artificial Intelligence and Machine Learning for Healthcare Solutions. 3rd ed. US: John Wiley & Sons, Inc., pp.281-291, 2021.
[20] SHarma, A., Bachate, R., V Pawar, M. and Shardoor, N., 2022. Artificial intelligence-based solutions for COVID-19. 3rd ed. US: Academic Press, pp.167-189, 2022.
[21] SHarma, A., Singh, P. and Dhar, G., 2021. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease. 5th ed. US: IGI Global, pp.140-149, 2021.
[22] Sharma, A. and Dhar, G., 2019. Deep Learning A Compass To Medical Sciences. Think India, 22(17), pp.1737-1746, 2019.
[23] Sharma, A. and Gurram, H., 2022. Identifying the Rules of Transition from AD to Dementia and MCI Using Machine Learning. International Journal of Control and Automation, [online] 13(4), pp.718 - 729. Available at: [Accessed 11 September 2022]
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
