ArticleOpen life sciences2023
Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach.
Article in Open life sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Quantifying Sagittal Craniosynostosis Severity: A Machine Learning Approach With CranioRate.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2026Article
- Improved prediction of childhood anemia using hybrid ensemble learning and dual-level explainability.Journal of public health research · 2026Article
- Artificial intelligence-powered models in predicting mortality in maternal, newborn, and children under five: a systematic review protocol.Systematic reviews · 2026Article
- Blockchain in Critical Care.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2025Review
- Trajectory of breastfeeding among Chinese women and risk prediction models based on machine learning: a cohort study.BMC pregnancy and childbirth · 2024Article
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Authors and funding
4 authors.
Funding
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Abstract
In developing countries, child health and restraining under-five child mortality are one of the fundamental concerns. UNICEF adopted sustainable development goal 3 (SDG3) to reduce the under-five child mortality rate globally to 25 deaths per 1,000 live births. The under-five mortality rate is 69 deaths per 1,000 live child-births in Pakistan as reported by the Demographic and Health Survey (2018). Predictive analytics has the power to transform the healthcare industry, personalizing care for every individual. Pakistan Demographic Health Survey (2017-2018), the publicly available dataset, is used in this study and multiple imputation methods are adopted for the treatment of missing values. The information gain, a feature selection method, ranked the information-rich features and examine their impact on child mortality prediction. The synthetic minority over-sampling method (SMOTE) balanced the training dataset, and four supervised machine learning classifiers have been used, namely the decision tree classifier, random forest classifier, naive Bayes classifier, and extreme gradient boosting classifier. For comparative analysis, accuracy, precision, recall, and
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.