Comparison Of Artificial Neural Network and Decision Tree Methods for Predicting the Maternal Outcome in A Tertiary Care Hospital in Odisha, India

Nihar Ranjan Panda; Jitendra Kumar  Pati; Tapasi  Pati; Soumya  Satpathy; Ruchi  Bhuyan

doi:10.55489/njcm.131120222262

Authors

Nihar Ranjan Panda IMS and Sum Hospital, SOA deemed to be University, Bhubaneswar; CV Raman Global University, Bhubaneswar, India
Jitendra Kumar Pati CV Raman Global University, Bhubaneswar, Odisha, India
Tapasi Pati IMS and Sum Hospital, SOA deemed to be University, Bhubaneswar, Odisha, India
Soumya Satpathy IMS and Sum Hospital, SOA deemed to be University, Bhubaneswar, Odisha, India
Ruchi Bhuyan IMS and Sum Hospital, SOA deemed to be University, Bhubaneswar, Odisha, India

DOI:

https://doi.org/10.55489/njcm.131120222262

Keywords:

Machine learning, maternal outcome, Classifier, Decision tree, ANN

Abstract

Background: This study used an artificial neural network (ANN) and a decision tree to predict maternal outcomes and their major determinants. An artificial neural network (ANN) and a decision tree were used in this study to determine maternal outcomes and their significant determinants.

Methods: Data was gathered from 955 pregnant women at a tertiary care hospital in Bhubaneswar, Odisha. A popular machine learning algorithm, artificial neural networks (ANN), was used to predict maternal outcomes and their determinants.

Results: in the bivariate analysis, we found gestational age is significantly associated with maternal outcome (p=<0.001). The accuracy of the ANN model and decision tree was 0.882 and 0.823, respectively. Based on the variable importance of ANN, the significant determinants of maternal outcome were birth weight, systolic blood pressure, hemoglobin, gestational age, age of mother, diastolic blood pressure etc.

Conclusion: This model can be utilized in future for Proper precautions and medical checkups required during the maternal period to avoid a negative maternal outcome.

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