Machine Learning Algorithms for understanding the determinants of under-five Mortality

Abstract

Background: Under-five mortality is a matter of serious concern for child health as well as the social development of any country. The paper aimed to find the accuracy of machine learning models in predicting under-five mortality and identify the most significant factors associated with under-five mortality.

Method: The data was taken from the National Family Health Survey (NFHS-IV) of Uttar Pradesh. First, we used multivariate logistic regression due to its capability for predicting the important factors, then we used machine learning techniques such as decision tree, random forest, Naïve Bayes, K- nearest neighbor (KNN), logistic regression, support vector machine (SVM), neural network, and ridge classifier. Each model's accuracy was checked by a confusion matrix, accuracy, precision, recall, F1 score, Cohen's Kappa, and area under the receiver operating characteristics curve (AUROC). Information gain rank was used to find the important factors for under-five mortality. Data analysis was performed using, STATA-16.0, Python 3.3, and IBM SPSS Statistics for Windows, Version 27.0 software.

Result: By applying the machine learning models, results showed that the neural network model was the best predictive model for under-five mortality when compared with other predictive models, with model accuracy of (95.29% to 95.96%), recall (71.51% to 81.03%), precision (36.64% to 51.83%), F1 score (50.46% to 62.68%), Cohen's Kappa value (0.48 to 0.60), AUROC range (93.51% to 96.22%) and precision-recall curve range (99.52% to 99.73%). The neural network was the most efficient model, but logistic regression also shows well for predicting under-five mortality with accuracy (94% to 95%)., AUROC range (93.4% to 94.8%), and precision-recall curve (99.5% to 99.6%). The number of living children, survival time, wealth index, child size at birth, birth in the last five years, the total number of children ever born, mother's education level, and birth order were identified as important factors influencing under-five mortality.

Conclusion: The neural network model was a better predictive model compared to other machine learning models in predicting under-five mortality, but logistic regression analysis also shows good results. These models may be helpful for the analysis of high-dimensional data for health research.

Keywords: Accuracy; Machine learning; Neural Network; Random Forest; Under-five mortality.

Fig. 1

Under-five mortality of Uttar Pradesh comparison graph of state-wise from (NFHS-4)

Fig. 2

Overview of the proposed framework of machine learning for under-five child mortality data

Fig. 3

ROC curve for machine learning models in predicting under-five mortality with all factors (70/30 Ratio)

Fig. 4

Precision-Recall curves for machine learning models in predicting under-five mortality with all factors (70/30 Ratio)\

Fig. 5

ROC curve for machine learning models in predicting under-five mortality with all factors (80/20 Ratio)

Fig. 6

Precision-Recall curve for machine learning models in predicting under-five mortality with all factors (80/20 Ratio)

Fig. 7

Information gain rank values of the variables under study

Fig. 8

ROC curve for machine learning models in predicting under-five mortality with important factors (70/30) Ratio

Fig. 9

Precision-Recall curve for machine learning models in predicting under-five mortality with important factors (70/30 Ratio)

Fig. 10

ROC curve for machine learning models in predicting under-five mortality with important factors (80/20 Ratio)

Fig. 11

Precision-Recall curve for machine learning models in predicting under-five mortality with important factors (80/20 Ratio)