Implementing Predictive Model for Low Birth Weight in Afghanistan

Conference proceedings article

Authors/Editors

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Strategic Research Themes

Assistive Technology for the Aged & Disabled & Rehabilitation (Smart Healthcare)

Publication Details

Author list: Zahirzada, Abdullah; Lavangnananda, Kittichai;

Publisher: Hindawi

Publication year: 2021

Start page: 67

End page: 72

Number of pages: 6

ISBN: 9781730000000

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105857749&doi=10.1109%2fKST51265.2021.9415792&partnerID=40&md5=16f10636829b221dafb4f5ca58ebe21b

Languages: English-Great Britain (EN-GB)

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Abstract

Birth weight is a significant determinant of the likelihood of survival of an infant. Low Birth Weight (LBW) has become an increasingly common problem, especially in developing and underdeveloped countries. Therefore, an ability to predict the LBW is beneficial and a good preventive measure. It is also a good indicator of future health risks of that infant. This study is concerned about the implementation of predictive LBW models for rural and urban areas of Afghanistan based on the data obtained from the Afghanistan Demographic and Health Survey 2015. The main objective of the study is to identify the most suitable machine learning techniques (i.e. classifiers) among the five popular ones. These are K-Nearest Neighbor (K-NN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine (SVM). Prior to implementing the predictive models, data preprocessing is carried out. This is done by means of data cleansing and feature selection. The well-known Correlation based Feature Selection algorithm (CFS) is employed to select the top ten attributes. The classifier in this study comprises two categories, Normal and LBW. The preparation of the dataset is carefully done to ensure well-balanced samples in each category. The study reveals that Random Forest is the best classifier with an accuracy of 84.70% and 85.2% and with the Area Under the Curve (AUC) of 91.0% and 92.1% for both rural and urban areas, respectively. The study has a direct benefit to the health and prevention policy making in Afghanistan. © 2021 IEEE.

Keywords

Afghanistan, Afghanistan Demographic and Health Survey, Classifiers, Correlation-based Feature Selection (CFS), Low Birth Weight (LBW), Machine Learning