Applications of Machine Learning Algorithms in Health Data Science (HDS) for Next Research Directions: A Survey Report

Year : 2024 | Volume :11 | Issue : 01 | Page : 16-21
By

Vinay Bhatt

Mayank Kumar

  1. Research Scholar Department of Computer Science and Engineering Asian International University, Imphal West Manipur India
  2. Associate Professor Department of Computer Science and Engineering Asian International University, Imphal West Manipur India

Abstract

At present time, data science is the big trend in computer science. The functioning of this technology is purely based on other advanced technology known as machine learning (ML). Data science and ML are subsets of artificial intelligence (AI). When a process of data science is used in healthcare systems, the new system is known as health data science (HDS). HDS is a branch of data science used to handle the large amount of data in the healthcare system. Recently, data science has been used to handle and analyze large volumes of data (structured or unstructured) with accuracy by using different techniques with algorithms of ML. This survey paper presented the ML applications in data science using different previous research. In this paper, firstly discuss the introduction of the paper with related information, secondly, discuss on review of literature on behalf of previous research, thirdly, discuss ML with its techniques and examples, fourthly, discuss on stages of data science, fifthly, discuss on weakness or research gaps of previous research works according to literature review and finally discuss on proposed work for next research directions using observations to research gaps.

Keywords: AI, ML, data science, health data science, supervised learning, unsupervised learning, reinforcement learning, deep learning, deep reinforcement learning, ANN

[This article belongs to Journal of Artificial Intelligence Research & Advances(joaira)]

How to cite this article: Vinay Bhatt, Mayank Kumar. Applications of Machine Learning Algorithms in Health Data Science (HDS) for Next Research Directions: A Survey Report. Journal of Artificial Intelligence Research & Advances. 2024; 11(01):16-21.
How to cite this URL: Vinay Bhatt, Mayank Kumar. Applications of Machine Learning Algorithms in Health Data Science (HDS) for Next Research Directions: A Survey Report. Journal of Artificial Intelligence Research & Advances. 2024; 11(01):16-21. Available from: https://journals.stmjournals.com/joaira/article=2024/view=131474

Browse Figures

References

  1. Martins RM, Gresse Von Wangenheim Findings on Teaching Machine Learning in High School: A Ten–Year Systematic Literature Review. Inform Educ. DOI: 10.15388/infedu.2023.18.
  2. He Y, Zhou Y, Wen T, Zhang S, Huang F, Zou X, Ma X, Zhu A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications. Appl Geochem. 2022;140:105273. DOI: 10.1016/j.apgeochem.2022.105273.
  3. Gandomi AH, Chen F, Abualigah Machine learning technologies for big data analytics. Electronics. 2022;11:421. DOI: 10.3390/electronics11030421.
  4. Liu T, Siegel E, Shen Deep learning and medical image analysis for COVID-19 diagnosis and prediction. Annu Rev Biomed Eng. 2022;24:179–201. DOI: 10.1146/annurev-bioeng-110220-012203, PubMed: 35316609.
  5. Kalaivaani PT, Krishnamoorthi R. Design and implementation of low power bio signal sensors for wireless body sensing network applications. Microprocess Microsyst. 2020;79:103271. DOI: 10.1016/j.micpro.2020.103271.
  6. Rao DMS, Sridhathri Diabetes mellitus prediction using ensemble machine learning techniques. ITM Web of Conferences. EDP Sciences. 2023;56. DOI: 10.1051/itmconf/20235605015.
  7. Viswanatha V, Ramachandra AC, Dhanush Murthy, Thanishka. Diabetes Prediction Using Machine Learning Approach. Strad Res. 2023;10(8):75-82. DOI: 10.37896/sr10.8/008.
  8. Wee BF, Sivakumar S, Lim KH, Wong WK, Juwono Diabetes detection based on machine learning and deep learning approaches. Multimed Tools Appl. 2023;83:24153–24185. DOI: 10.1007/s11042-023-16407-5.
  9. Madhu B, Aerranagula V, Mahomad R, Ravindernaik V, Madhavi K, Krishna Techniques of machine learning for the purpose of predicting diabetes risk in PIMA Indians. E3S Web of Conferences. EDP Sciences. 2023;430. DOI: 10.1051/e3sconf/202343001151.
  10. Sharma A, Mishra Performance analysis of machine learning based optimized feature selection approaches for breast cancer diagnosis. Int J Inf Technol. 2022;14:1949–1960. DOI: 10.1007/s41870-021-00671-5.
  11. Zhang Q, Gao J, Wu JT, Cao Z, Dajun Zeng Data science approaches to confronting the COVID-19 pandemic: A narrative review. Philos Trans A Math Phys Eng Sci. 2022;380:20210127. DOI: 10.1098/rsta.2021.0127.
  12. Kumar S, Sharma D, Rao S, Lim WM, Mangla Past, present, and future of sustainable finance: Insights from big data analytics through machine learning of scholarly research. Ann Oper Res. 2022:1–44. DOI: 10.1007/s10479-021-04410-8.
  13. Zeng Z, Li Y, Li Y, Luo Statistical and machine learning methods for spatially resolved transcriptomics data analysis. Genome Biol. 2022;23:83. DOI: 10.1186/s13059-022-02653-7.
  14. Khan K, Ahmad W, Amin MN, Ahmad A systematic review of the research development on the application of machine learning for concrete. Materials. 2022;15:4512. DOI: 10.3390/ma15134512.
  15. Manley K, Nyelele C, Egoh A review of machine learning and big data applications in addressing ecosystem service research gaps. Ecosyst Serv. 2022;57:101478. DOI: 10.1016/j.ecoser.2022.101478.
  16. Sarker Machine learning: Algorithms, real-world applications and research directions. SN Comput Sci. 2021;2:160. DOI: 10.1007/s42979-021-00592-x, PubMed: 33778771.
  17. Shahraki A, Abbasi M, Taherkordi A, Jurcut A comparative study on online machine learning techniques for network traffic streams analysis. Comput Networks. 2022;207:108836. DOI: 10.1016/j.comnet.2022.108836.
  18. Mahesh Machine learning algorithms-a review. Int J Sci Res. 2020;9:381–386.
  19. Jin Research on machine learning and its algorithms and development. J Phys Conf Ser. IOP Publishing. 2020;1544(1):012003. DOI: 10.1088/1742-596/1544/1/012003.
  20. Pandey D, Niwaria K, Chourasia Machine Learning Algorithms: A Review. Feb 2019;6(2):916–922.
  21. Ray S. A Quick Review of Machine Learning 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India. 2019. pp. 35-39. DOI: 10.1109/COMITCon.2019.8862451.
  22. Divya KS, Bhargavi P, Jyothi Machine learning algorithms in big data analytics. Int J Comput Sci Eng. 2018;6:63–70. DOI: 10.26438/ijcse/v6i1.6370.
Regular Issue Subscription Review Article
Volume 11
Issue 01
Received December 29, 2023
Accepted January 4, 2024
Published January 17, 2024
Views: 126