ISSN 1119-4618
 

Original Research 


Predicting Child Delivery Mode Using Data Mining by Considering Maternal and Fetal Health Conditions

Fatsuma Jauro, Baththama Bello Alhassan, Aliyu Garba, Shehu Muhammad Toro.

Abstract
In maternity care, deciding which method to use sometimes depends on the interest of the mother. In other cases, the mode of delivery is decided based on the observed health condition of the mother and the fetus. Predicting mode of delivery before term would help reduce the excessive and insignificant usage of operative procedures. In this work, data mining classification models have been used to predict mode of delivery in obstetrics by considering both maternal and fetal factors. Particularly, K-Nearest Neighbor (KNN), Nave Bayes (NB), Support Vector Machine (SVM) and Decision Trees (DT) models were used. The data used is a real dataset obtained from the maternity unit of Ahmadu Bello University Teaching Hospital (ABUTH), Zaria. All the used models were found to be efficient in predicting the mode of delivery as none has less than 90% accuracy. However, NB was found to be the best amongst all with an accuracy of 99.78% and KNN being the least but still with an accuracy of 91.41%.

Key words: Maternity, Delivery, Prediction, Models, Accuracy;


 
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How to Cite this Article
Pubmed Style

FJ, Alhassan BB, Garba A, Toro SM. Predicting Child Delivery Mode Using Data Mining by Considering Maternal and Fetal Health Conditions. JPAS. 2019; 18(1): 31-35. doi:10.5455/sf.28491


Web Style

FJ, Alhassan BB, Garba A, Toro SM. Predicting Child Delivery Mode Using Data Mining by Considering Maternal and Fetal Health Conditions. http://www.atbuscienceforum.com/?mno=28491 [Access: October 16, 2019]. doi:10.5455/sf.28491


AMA (American Medical Association) Style

FJ, Alhassan BB, Garba A, Toro SM. Predicting Child Delivery Mode Using Data Mining by Considering Maternal and Fetal Health Conditions. JPAS. 2019; 18(1): 31-35. doi:10.5455/sf.28491



Vancouver/ICMJE Style

FJ, Alhassan BB, Garba A, Toro SM. Predicting Child Delivery Mode Using Data Mining by Considering Maternal and Fetal Health Conditions. JPAS. (2019), [cited October 16, 2019]; 18(1): 31-35. doi:10.5455/sf.28491



Harvard Style

, F. J., Alhassan, . B. B., Garba, . A. & Toro, . S. M. (2019) Predicting Child Delivery Mode Using Data Mining by Considering Maternal and Fetal Health Conditions. JPAS, 18 (1), 31-35. doi:10.5455/sf.28491



Turabian Style

, Fatsuma Jauro, Baththama Bello Alhassan, Aliyu Garba, and Shehu Muhammad Toro. 2019. Predicting Child Delivery Mode Using Data Mining by Considering Maternal and Fetal Health Conditions. Science Forum (Journal of Pure and Applied Sciences), 18 (1), 31-35. doi:10.5455/sf.28491



Chicago Style

, Fatsuma Jauro, Baththama Bello Alhassan, Aliyu Garba, and Shehu Muhammad Toro. "Predicting Child Delivery Mode Using Data Mining by Considering Maternal and Fetal Health Conditions." Science Forum (Journal of Pure and Applied Sciences) 18 (2019), 31-35. doi:10.5455/sf.28491



MLA (The Modern Language Association) Style

, Fatsuma Jauro, Baththama Bello Alhassan, Aliyu Garba, and Shehu Muhammad Toro. "Predicting Child Delivery Mode Using Data Mining by Considering Maternal and Fetal Health Conditions." Science Forum (Journal of Pure and Applied Sciences) 18.1 (2019), 31-35. Print. doi:10.5455/sf.28491



APA (American Psychological Association) Style

, F. J., Alhassan, . B. B., Garba, . A. & Toro, . S. M. (2019) Predicting Child Delivery Mode Using Data Mining by Considering Maternal and Fetal Health Conditions. Science Forum (Journal of Pure and Applied Sciences), 18 (1), 31-35. doi:10.5455/sf.28491