Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset
Abstract Background Under-five mortality remains a significant public health issue in developing countries. This study aimed to assess the effectiveness of various machine learning algorithms in predicting under-five mortality in Nigeria and identify the most relevant predictors. Methods The study u...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-03-01
|
| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12911-024-02476-5 |
| _version_ | 1827301109005287424 |
|---|---|
| author | Oduse Samuel Temesgen Zewotir Delia North |
| author_facet | Oduse Samuel Temesgen Zewotir Delia North |
| author_sort | Oduse Samuel |
| collection | DOAJ |
| description | Abstract Background Under-five mortality remains a significant public health issue in developing countries. This study aimed to assess the effectiveness of various machine learning algorithms in predicting under-five mortality in Nigeria and identify the most relevant predictors. Methods The study used nationally representative data from the 2018 Nigeria Demographic and Health Survey. The study evaluated the performance of the machine learning models such as the artificial neural network, k-nearest neighbourhood, Support Vector Machine, Naïve Bayes, Random Forest, and Logistic Regression using the true positive rate, false positive rate, accuracy, precision, F-measure, Matthew’s correlation coefficient, and the Area Under the Receiver Operating Characteristics. Results The study found that machine learning models can accurately predict under-five mortality, with the Random Forest and Artificial Neural Network algorithms emerging as the best models, both achieving an accuracy of 89.47% and an AUROC of 96%. The results show that under-five mortality rates vary significantly across different characteristics, with wealth index, maternal education, antenatal visits, place of delivery, employment status of the woman, number of children ever born, and region found to be the top determinants of under-five mortality in Nigeria. Conclusions The findings suggest that machine learning models can be useful in predicting U5M in Nigeria with high accuracy. The study emphasizes the importance of addressing social, economic, and demographic disparities among the population in Nigeria. The study’s findings can inform policymakers and health workers about developing targeted interventions to reduce under-five mortality in Nigeria. |
| first_indexed | 2024-04-24T16:17:28Z |
| format | Article |
| id | doaj.art-548b5e7814ca450284bf418f6c0b1c01 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-24T16:17:28Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-548b5e7814ca450284bf418f6c0b1c012024-03-31T11:22:37ZengBMCBMC Medical Informatics and Decision Making1472-69472024-03-0124111310.1186/s12911-024-02476-5Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 datasetOduse Samuel0Temesgen Zewotir1Delia North2School of Mathematics, Statistics and Computer Science, University of KwaZulu-NatalSchool of Mathematics, Statistics and Computer Science, University of KwaZulu-NatalSchool of Mathematics, Statistics and Computer Science, University of KwaZulu-NatalAbstract Background Under-five mortality remains a significant public health issue in developing countries. This study aimed to assess the effectiveness of various machine learning algorithms in predicting under-five mortality in Nigeria and identify the most relevant predictors. Methods The study used nationally representative data from the 2018 Nigeria Demographic and Health Survey. The study evaluated the performance of the machine learning models such as the artificial neural network, k-nearest neighbourhood, Support Vector Machine, Naïve Bayes, Random Forest, and Logistic Regression using the true positive rate, false positive rate, accuracy, precision, F-measure, Matthew’s correlation coefficient, and the Area Under the Receiver Operating Characteristics. Results The study found that machine learning models can accurately predict under-five mortality, with the Random Forest and Artificial Neural Network algorithms emerging as the best models, both achieving an accuracy of 89.47% and an AUROC of 96%. The results show that under-five mortality rates vary significantly across different characteristics, with wealth index, maternal education, antenatal visits, place of delivery, employment status of the woman, number of children ever born, and region found to be the top determinants of under-five mortality in Nigeria. Conclusions The findings suggest that machine learning models can be useful in predicting U5M in Nigeria with high accuracy. The study emphasizes the importance of addressing social, economic, and demographic disparities among the population in Nigeria. The study’s findings can inform policymakers and health workers about developing targeted interventions to reduce under-five mortality in Nigeria.https://doi.org/10.1186/s12911-024-02476-5Under-five mortalityMachine learningNigeriaDemographic and health surveysDecision-making tools |
| spellingShingle | Oduse Samuel Temesgen Zewotir Delia North Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset BMC Medical Informatics and Decision Making Under-five mortality Machine learning Nigeria Demographic and health surveys Decision-making tools |
| title | Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset |
| title_full | Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset |
| title_fullStr | Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset |
| title_full_unstemmed | Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset |
| title_short | Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset |
| title_sort | application of machine learning methods for predicting under five mortality analysis of nigerian demographic health survey 2018 dataset |
| topic | Under-five mortality Machine learning Nigeria Demographic and health surveys Decision-making tools |
| url | https://doi.org/10.1186/s12911-024-02476-5 |
| work_keys_str_mv | AT odusesamuel applicationofmachinelearningmethodsforpredictingunderfivemortalityanalysisofnigeriandemographichealthsurvey2018dataset AT temesgenzewotir applicationofmachinelearningmethodsforpredictingunderfivemortalityanalysisofnigeriandemographichealthsurvey2018dataset AT delianorth applicationofmachinelearningmethodsforpredictingunderfivemortalityanalysisofnigeriandemographichealthsurvey2018dataset |