Rakesh Kumar Saroj1, Pawan Kumar Yadav2, Rajneesh Singh3, Obvious N Chilyabanyama4,5. 1. Department of Community Medicine, Sikkim Manipal Institute of Medical Sciences-Sikkim Manipal University, Gangtok, Sikkim, 737102, India. rakesh.saroj@bhu.ac.in. 2. Department of Biostatistics and Epidemiology, International Institute for Population Sciences, Mumbai, 400088, India. 3. Department of Mathematics and Statistics, Banasthali Vidyapith, Vanasthali Rd, Aliyabad, Tonk, Rajasthan, 304022, India. 4. Centre for Infectious Disease Research in Zambia, Lusaka, Zambia. 5. African Centre of Excellency in Data Science (ACEDS), University of Rwanda, KK 737 Street, Gikondo, Kigali, Rwanda.
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.
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.
Authors: Ashley A Campbell; Saskia de Pee; Kai Sun; Klaus Kraemer; Andrew Thorne-Lyman; Regina Moench-Pfanner; Mayang Sari; Nasima Akhter; Martin W Bloem; Richard D Semba Journal: Food Nutr Bull Date: 2009-06 Impact factor: 2.069
Authors: Vivek V Shukla; Barry Eggleston; Namasivayam Ambalavanan; Elizabeth M McClure; Musaku Mwenechanya; Elwyn Chomba; Carl Bose; Melissa Bauserman; Antoinette Tshefu; Shivaprasad S Goudar; Richard J Derman; Ana Garcés; Nancy F Krebs; Sarah Saleem; Robert L Goldenberg; Archana Patel; Patricia L Hibberd; Fabian Esamai; Sherri Bucher; Edward A Liechty; Marion Koso-Thomas; Waldemar A Carlo Journal: JAMA Netw Open Date: 2020-11-02