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.

Syeda Sajida Hussain, Tooba Fatima, Rabia Riaz, Sanam Shahla Rizvi, Farina Riaz and Se Jin Kwon, “A Comparative Study of Supervised Machine Learning Techniques for Diagnosing Mode of Delivery in Medical Sciences” International Journal of Advanced Computer Science and Applications(IJACSA), 10(12), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101216

@article{Hussain2019,
title = {A Comparative Study of Supervised Machine Learning Techniques for Diagnosing Mode of Delivery in Medical Sciences},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101216},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101216},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {12},
author = {Syeda Sajida Hussain and Tooba Fatima and Rabia Riaz and Sanam Shahla Rizvi and Farina Riaz and Se Jin Kwon}
}