Implementing Predictive Model for Child Mortality in Afghanistan
Conference proceedings article
ผู้เขียน/บรรณาธิการ
ไม่พบข้อมูลที่เกี่ยวข้อง
กลุ่มสาขาการวิจัยเชิงกลยุทธ์
รายละเอียดสำหรับงานพิมพ์
รายชื่อผู้แต่ง: Nasrat, Nasratullah; Lavangnananda, Kittichai
ผู้เผยแพร่: Springer Science and Business Media Deutschland GmbH
ปีที่เผยแพร่ (ค.ศ.): 2021
Volume number: 217
หน้าแรก: 331
หน้าสุดท้าย: 342
จำนวนหน้า: 12
ISBN: 9789811621017
นอก: 23673370
ภาษา: English-Great Britain (EN-GB)
บทคัดย่อ
Reduction of child mortality and improving child health are health priorities in underdeveloped and developing countries. Afghanistan has a high rank of child mortality. Therefore, the ability to predict child mortality is beneficial and a good preventive measure. This study aims to develop a child mortality predictive model by utilizing data mining classification algorithms and identify the most suitable classifier among the five popular machine learning techniques. These are K-Nearest Neighbors (K-NN), Naïve Bayes (NB), Neural Network (NN), Random Forest (RF), and Support Vector Machine (SVM). The dataset used is the Afghanistan Demographic and Health Survey (AfDHS). The well-known Correlation-based Feature Selection (CFS) algorithm is employed to select the top 13 attributes during the data preprocessing. The classification in this study comprises two categories, ‘Alive’ and ‘Dead’. Preparation of the dataset is carefully done to ensure well-balanced samples in each category. The validation of the predictive models is assessed by means of Accuracy, Precision, Recall, and Area Under the Receiver Operating Characteristic Curve (AUC). The study reveals that Random Forest is the best classifier. The result obtained from this study can be beneficial to child health improvement programs in Afghanistan as well as in policy-making, especially when resources are limited. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
คำสำคัญ
Afghanistan, Afghanistan Demographic and Health Survey, Child mortality, Classifiers, Data Mining
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