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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(5): 677-680 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.213

Automated Diagnosis and Cause Analysis of Cesarean Section Using Machine Learning Techniques

Ayesha Sana, Saad Razzaq, and Javed Ferzund

Abstract—Machine learning techniques provide learning mechanism that can be used to induce knowledge from data. A few studies exist on the use of machine learning techniques for medical diagnosis, prediction and treatment. In this study we evaluate different machine learning techniques for birth classification (cesarean or normal). Data on cesarean section is collected and different medical factors are identified that result in cesarean births. A birth classification model is built using decision tree and artificial neural networks. It can classify the births into normal and cesarean with an average accuracy, precision and recall of 80%, 85% and 84% respectively. Association rule mining is used to extract disease patterns from the collected data. It highlights the important medical factors that are associated with cesarean births.

Index Terms—Birth classification, cesarean section, machine learning, medical diagnosis.

The authors are with the Computer Science Department, University of Sargodha, Sargodha, Pakistan (e-mail: asana@uos.edu.pk; saadrazzaq@uos.edu.pk; jferzund@uos.edu.pk).


Cite: Ayesha Sana, Saad Razzaq, and Javed Ferzund, "Automated Diagnosis and Cause Analysis of Cesarean Section Using Machine Learning Techniques," International Journal of Machine Learning and Computing vol. 2, no. 5, pp. 677-680, 2012.

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