Application of machine learning methods for predicting under-five mortality : analysis of Nigerian demographic health survey 2018 dataset
© 2024. The Author(s)..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
|---|---|
| Enthalten in: |
BMC medical informatics and decision making - 24(2024), 1 vom: 25. März, Seite 86 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Samuel, Oduse [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Decision-making tools |
|---|
| Anmerkungen: |
Date Completed 27.03.2024 Date Revised 28.03.2024 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1186/s12911-024-02476-5 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM370180496 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM370180496 | ||
| 003 | DE-627 | ||
| 005 | 20240329001007.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240327s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1186/s12911-024-02476-5 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1353.xml |
| 035 | |a (DE-627)NLM370180496 | ||
| 035 | |a (NLM)38528495 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Samuel, Oduse |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Application of machine learning methods for predicting under-five mortality |b analysis of Nigerian demographic health survey 2018 dataset |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 27.03.2024 | ||
| 500 | |a Date Revised 28.03.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2024. The Author(s). | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Decision-making tools | |
| 650 | 4 | |a Demographic and health surveys | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Nigeria | |
| 650 | 4 | |a Under-five mortality | |
| 700 | 1 | |a Zewotir, Temesgen |e verfasserin |4 aut | |
| 700 | 1 | |a North, Delia |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t BMC medical informatics and decision making |d 2001 |g 24(2024), 1 vom: 25. März, Seite 86 |w (DE-627)NLM112392881 |x 1472-6947 |7 nnns |
| 773 | 1 | 8 | |g volume:24 |g year:2024 |g number:1 |g day:25 |g month:03 |g pages:86 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1186/s12911-024-02476-5 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 24 |j 2024 |e 1 |b 25 |c 03 |h 86 | ||