Machine Learning in Public Health: A Review

Machine Learning in Public Health: A Review

Md. Asadullah

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Mamunar Rashid

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Priyanka Bosu

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Emon Ahmed

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Sabeha Tamanna

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Gopalganj Science and Technology University

Machine Learning in Public Health: A Review

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Abstract

In recent years Machine learning has been used for disease diagnosis and prediction in the public healthcare sector. It plays an essential role in healthcare and is rapidly being applied to education. It is one of the driving forces in science and technology, but the emergence of big data involves paradigm shifts in the implementation of machine learning techniques from traditional methods. Computers are now well equipped to diagnose many health issues with large health care datasets and progressions in machine learning techniques. Researchers have been used several machine learning techniques in public health. Several methods, including Support Vector Machines (SVM), Decision Trees (DT), Naïve Bayes (NB), Random Forest (RF), and K-Nearest Neighbors (KNN), are widely used in predictive model design research, resulting in effective and accurate decision-making. The predictive models discussed here are based on different supervised ML techniques and various input characteristics and data samples. Therefore, the predictive models can be used to support healthcare professionals and patients globally to improve public health as well as global health. Finally, we provide some problems and challenges which face the researcher in public health.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Md. Asadullah. 2021. \u201cMachine Learning in Public Health: A Review\u201d. Global Journal of Research in Engineering - F: Electrical & Electronic GJRE-F Volume 21 (GJRE Volume 21 Issue F3).

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GJRE Volume 21 Issue F3

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Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

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GJRE-F Classification FOR Code: 170203

090699

Submission ReceivedJune 13, 2021
Peer Review Double Blind
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Accepted July 1, 2021
Published July 15, 2021

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v1.2

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August 25, 2021

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