Machine Learning in Healthcare: A Review
Abstract
Machine Learning is modern and highly sophisticated technological applications became a huge trend in the industry. Machine Learning is Omni present and is widely used in various applications. It is playing a vital role in many fields like finance, Medical science and in security. Machine learning is used to discover patterns from medical data sources and provide excellent capabilities to predict diseases. In this paper, we review various machine learning algorithms used for developing efficient decision support for healthcare applications. This paper helps in reducing the research gap for building efficient decision support system for medical applications.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Machine learning provides outstanding performance on tasks that used to require a lot of knowledge and time to model.
“the breakthrough in Machine Learning (ML) that provide outstanding performance tasks that used to require a lot of knowledge and time to model [37]”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [37]
Cites
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287 · see citing works
Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972 · see citing works
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An exhaustive survey on security and privacy issues in Healthcare 4.0
Hathaliya, J. J., & Tanwar, S. (2020) · Computer Communications · doi:10.1016/j.comcom.2020.02.018 · Cited by 462 · see citing works
Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review
Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J., & McDonald-Maier, K. D. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103197 · Cited by 180 · see citing works
“Besides, the breakthrough in Machine Learning (ML) that provide outstanding performance tasks that used to require a lot of knowledge and time to model [37], as well as the tremendous advances made in processing system technologies that made the ML computing possible have given researchers more tools and resources to…”
BackgroundA Review of Machine Learning and TinyML in Healthcare
Tsoukas, V., Boumpa, E., Giannakas, G., & Kakarountas, A. (2021) · 25th Pan-Hellenic Conference on Informatics · doi:10.1145/3503823.3503836 · Cited by 79 · see citing works
Kantidakis, G., Putter, H., Lancia, C., Boer, J. D., Braat, A. E., & Fiocco, M. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-01153-1 · Cited by 75 · see citing works
- Authors
- Shailaja, K., Seetharamulu, B., & Jabbar, M. A.
- Affiliations
- Vardhaman College of Engineering (IN)
- Venue
- 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA)
- Publisher
- IEEE
- Published
- 2018-03
- Pages
- 910-914
- OpenAlex
- W2897093100
- Cited by
- 372
- References
- 2
- Citations by year
- 2026: 19 · 2025: 50 · 2024: 84 · 2023: 86 · 2022: 72 · 2021: 44 · 2020: 14 · 2019: 3
- FWCI
- 17.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Electronics, Communication and Aerospace Technology (ICECA) (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- machine learning in healthcare, clinical decision support systems, disease prediction, medical data analysis, pattern discovery, medical applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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