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A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

Published on: January 12, 2018

Using an innovative stacked ensemble algorithm for the accurate prediction of preterm birth

Pari Ramalingam¹, Maheshwari Sandhya¹, Sharmila Sankar¹

¹Department of Computer Science and Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India

Journal of the Turkish German Gynecological Association

|December 4, 2018

Objective:

A birth before the normal term of 38 weeks of gestation is called a preterm birth (PTB). It is one of the major reasons for neonatal death. The objective of this article was to predict PTB well in advance so that it was converted to a term birth.

Material And Methods:

This study uses the historical data of expectant mothers and an innovative stacked ensemble (SE) algorithm to predict PTB. The proposed algorithm stacks classifiers in multiple tiers. The accuracy of the classiffication is improved in every tier.

Results:

The experimental results from this study show that PTB can be predicted with more than 96% accuracy using innovative SE learning.

Conclusion:

The proposed approach helps physicians in Gynecology and Obstetrics departments to decide whether the expectant mother needs treatment. Treatment can be given to delay the birth only in patients for whom PTB is predicted, or in many cases to convert the PTB to a normal birth. This, in turn, can reduce the mortality of babies due to PTB.

Keywords:

Preterm birth neonatal death stacked ensemble stacked generalization meta-learning risk factors of preterm birth

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