| Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach |
| Authors: |
Farrukh Iqbal, Muhammad Islam Satti, Azeem Irshad & Mohd Asif Shah |
| Source: |
Open Life Sciences, 18 |
| Topic(s): |
Child health
Childhood mortality
Modelling
|
| Country: |
Asia
Pakistan
|
| Published: |
JUL 2023 |
| 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 F1-score have been used. Eventually, a predictive analytics framework is built that predicts whether the child is alive or dead. The number under-five children in a household, preceding birth interval, family members, mother age, age of mother at first birth, antenatal care visits, breastfeeding, child size at birth, and place of delivery were found to be critical risk factors for child mortality. The random forest classifier performed efficiently and predicted under-five child mortality with accuracy (93.8%), precision (0.964), recall (0.971), and F1-score (0.967). The findings could greatly assist child health intervention programs in decision-making. |
| Web: |
https://www.degruyter.com/document/doi/10.1515/biol-2022-0609/html |
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