Open Access

Intelligent Machine Learning Frameworks for Health Data Analytics: Emerging Trends and Future Research Opportunities

https://doi.org/10.55640/ijaair-v03i10-02

PDF

Faisal Al-Harbi ¹ , Noor Al-Qahtani ¹ ,

⁴ Department of AI and Data Science, Arabian Institute of Smart Technologies, Riyadh, Saudi Arabia

⁴ College of Computing and Artificial Intelligence, Gulf Research University, Jeddah, Saudi Arabia

Abstract

Health Data Analytics (HDA) has emerged as a critical domain for extracting actionable knowledge from rapidly growing healthcare datasets. The integration of machine learning (ML) techniques with health data science has significantly enhanced disease prediction, medical image analysis, patient monitoring, and clinical decision support systems. This research and review paper examines intelligent machine learning frameworks employed in health data analytics, emphasizing their theoretical foundations, applications, emerging trends, and future research opportunities. A comprehensive synthesis of existing studies is conducted to evaluate supervised learning, unsupervised learning, ensemble methods, deep learning architectures, and data-driven analytical models in healthcare environments. The review identifies major challenges related to data quality, interpretability, scalability, privacy, and model generalization. Furthermore, a conceptual framework is proposed to guide future research in intelligent healthcare analytics. The findings indicate that machine learning-driven health data systems can significantly improve predictive accuracy and healthcare efficiency, although challenges concerning transparency, ethical deployment, and real-time implementation remain unresolved.

Keywords

Health Data Analytics, Machine Learning, Health Data Science

References

Divya KS, Bhargavi P, Jyothi Machine learning algorithms in big data analytics. Int J Comput Sci Eng. 2018;6:63–70.

Gandomi AH, Chen F, Abualigah Machine learning technologies for big data analytics. Electronics. 2022;11:421.

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.

Jin Research on machine learning and its algorithms and development. J Phys Conf Ser. IOP Publishing. 2020;1544(1):012003.

Kalaivaani PT, Krishnamoorthi R. Design and implementation of low power bio signal sensors for wireless body sensing network applications. Microprocess Microsyst. 2020;79:103271.

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.

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.

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. PubMed: 35316609.

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.

Mahesh Machine learning algorithms-a review. Int J Sci Res. 2020;9:381–386.

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.

Martins RM, Gresse Von Wangenheim Findings on Teaching Machine Learning in High School: A Ten–Year Systematic Literature Review. Inform Educ.

Pandey D, Niwaria K, Chourasia Machine Learning Algorithms: A Review. Feb 2019;6(2):916–922.

Rao DMS, Sridhathri Diabetes mellitus prediction using ensemble machine learning techniques. ITM Web of Conferences. EDP Sciences. 2023;56.

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.

Sarker Machine learning: Algorithms, real-world applications and research directions. SN Comput Sci. 2021;2:160. PubMed: 33778771.

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.

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.

Viswanatha V, Ramachandra AC, Dhanush Murthy, Thanishka. Diabetes Prediction Using Machine Learning Approach. Strad Res. 2023;10(8):75-82.

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.

Zeng Z, Li Y, Li Y, Luo Statistical and machine learning methods for spatially resolved transcriptomics data analysis. Genome Biol. 2022;23:83.

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.

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >>

International Journal of Advanced Artificial Intelligence Research

Intelligent Machine Learning Frameworks for Health Data Analytics: Emerging Trends and Future Research Opportunities

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles