A comparative study of supervised machine learning techniques for diagnosing mode of delivery in medical sciences

Article


Hussain, Syeda Sajida, Riaz, Rabia, Fatima, Tooba, Rizvi, Sanam Shehla, Riaz, Farina and Kwon, Se Jin. 2019. "A comparative study of supervised machine learning techniques for diagnosing mode of delivery in medical sciences." International Journal of Advanced Computer Science and Applications. 10 (12), pp. 120-125. https://doi.org/10.14569/IJACSA.2019.0101216
Article Title

A comparative study of supervised machine learning techniques for diagnosing mode of delivery in medical sciences

ERA Journal ID210614
Article CategoryArticle
AuthorsHussain, Syeda Sajida (Author), Riaz, Rabia (Author), Fatima, Tooba (Author), Rizvi, Sanam Shehla (Author), Riaz, Farina (Author) and Kwon, Se Jin
EditorsHussain, Syeda Sajida
Journal TitleInternational Journal of Advanced Computer Science and Applications
Journal Citation10 (12), pp. 120-125
Number of Pages6
Year2019
Place of PublicationBradford, United Kingdom
ISSN2156-5570
2158-107X
Digital Object Identifier (DOI)https://doi.org/10.14569/IJACSA.2019.0101216
Web Address (URL)https://www.researchgate.net/publication/338428601_A_Comparative_Study_of_Supervised_Machine_Learning_Techniques_for_Diagnosing_Mode_of_Delivery_in_Medical_Sciences
Abstract

The uses of machine learning techniques in medical diagnosis are very helpful tools now-a-days. By using machine learning algorithms and techniques, many complex medical problems can be solved easily and quickly. Without these techniques, it was a difficult task to find the causes of a problem or to suggest most appropriate solution for the problem with high accuracy. The machine learning techniques are used in almost every field of medical sciences such as heart diseases, diabetes, cancer prediction, blood transfusion, gender prediction and many more. Both supervised and unsupervised machine learning techniques are applied in the field of medical and health sciences to find the best solution for any medical illness. In this paper, the implementation of supervised machine learning techniques is performed for classifying the data of the pregnant women on the basis of mode of delivery either it will be a C-Section or a normal delivery. This analysis allows classifying the subjects into caesarean and normal delivery cases, hence providing the insight to physician to take precautionary measures to ensure the health of an expecting mother and an expected child.

Keywordsmachine learning; supervised bioinformatics; medical sciences
ANZSRC Field of Research 2020420302. Digital health
420308. Health informatics and information systems
Byline AffiliationsUniversity of Azad Jammu and Kashmir, Pakistan
DataCheck, Pakistan
Raptor Interactive, South Africa
Independent Researcher, Australia
Kangwon National University, Korea
Institution of OriginUniversity of Southern Queensland
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