Journal of Artificial Intelligence Research & Advances Review Article

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

  1. Vinay Bhatt Department of Computer Science and Engineering Asian International University, Imphal West
  2. Mayank Kumar Department of Computer Science and Engineering Asian International University, Imphal West

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

References (22)

  1. Martins RM, Gresse Von Wangenheim C. Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review. Informatics in Education. 2022. doi:10.15388/infedu.2023.18
  2. He Y, Zhou Y, Wen T, Zhang S, Huang F, Zou X, et al. A review of machine learning in geochemistry and cosmochemistry: Method improvements and applications. Applied Geochemistry. 2022;140:105273. doi:10.1016/j.apgeochem.2022.105273
  3. Gandomi AH, Chen F, Abualigah L. Machine Learning Technologies for Big Data Analytics. Electronics. 2022;11(3):421. doi:10.3390/electronics11030421
  4. Liu T, Siegel E, Shen D. Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction. Annual Review of Biomedical Engineering. 2022;24(1):179-201. doi:10.1146/annurev-bioeng-110220-012203
  5. Kalaivaani PT, Krishnamoorthi R. Design and implementation of low power bio signal sensors for wireless body sensing network applications. Microprocessors and Microsystems. 2020;79:103271. doi:10.1016/j.micpro.2020.103271
  6. Rao DMS, Sridhathri DS. Diabetes Mellitus Prediction Using Ensemble Machine Learning Techniques. ITM Web of Conferences. 2023;56:05015. 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 FH. Diabetes detection based on machine learning and deep learning approaches. Multimedia Tools and Applications. 2023;83(8):24153-24185. doi:10.1007/s11042-023-16407-5
  9. Madhu B, Aerranagula V, Mahomad R, Ravindernaik V, Madhavi K, Krishna G. Techniques of Machine Learning for the Purpose of Predicting Diabetes Risk in PIMA Indians. E3S Web of Conferences. 2023;430:01151. doi:10.1051/e3sconf/202343001151
  10. Sharma A, Mishra PK. Performance analysis of machine learning based optimized feature selection approaches for breast cancer diagnosis. International Journal of Information Technology. 2021;14(4):1949-1960. doi:10.1007/s41870-021-00671-5
  11. Zhang Q, Gao J, Wu JT, Cao Z, Dajun Zeng D. Data science approaches to confronting the COVID-19 pandemic: a narrative review. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2021;380(2214). doi:10.1098/rsta.2021.0127
  12. Kumar S, Sharma D, Rao S, Lim WM, Mangla SK. Past, present, and future of sustainable finance: insights from big data analytics through machine learning of scholarly research. Annals of Operations Research. 2022;345(2-3):1061-1104. doi:10.1007/s10479-021-04410-8
  13. Zeng Z, Li Y, Li Y, Luo Y. Statistical and machine learning methods for spatially resolved transcriptomics data analysis. Genome Biology. 2022;23(1). doi:10.1186/s13059-022-02653-7
  14. Khan K, Ahmad W, Amin MN, Ahmad A. A Systematic Review of the Research Development on the Application of Machine Learning for Concrete. Materials. 2022;15(13):4512. doi:10.3390/ma15134512
  15. Manley K, Nyelele C, Egoh BN. A review of machine learning and big data applications in addressing ecosystem service research gaps. Ecosystem Services. 2022;57:101478. doi:10.1016/j.ecoser.2022.101478
  16. Sarker IH. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science. 2021;2(3). doi:10.1007/s42979-021-00592-x
  17. Shahraki A, Abbasi M, Taherkordi A, Jurcut AD. A comparative study on online machine learning techniques for network traffic streams analysis. Computer 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 Algorithms. 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon). 2019:35-39. doi:10.1109/comitcon.2019.8862451
  22. Divya KS, Department of Computer Science, Sri Padmavathi Mahila Viswavidhyalayam, Tirupati, India, Bhargavi P, Department of Computer Science, Sri Padmavathi Mahila Viswavidhyalayam, Tirupati, India, Jyothi S, Department of Computer Science, Sri Padmavathi Mahila Viswavidhyalayam, Tirupati, India. Machine Learning Algorithms in Big data Analytics. International Journal of Computer Sciences and Engineering. 2018;6(1):63-70. doi:10.26438/ijcse/v6i1.6370
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