Hussain, Syeda Sajida and Riaz, Rabia and Fatima, Tooba and Rizvi, Sanam Shehla and Riaz, Farina 
ORCID: https://orcid.org/0000-0001-9223-7117 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.
ISSN 2158-107X
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Text (Published Version)
A Comparative Study of Supervised Machine.pdf Available under License Creative Commons Attribution 4.0. Download (549kB) | Preview |
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.
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