Improved Text Analysis Approach for Predicting Effects of Nutrient on Human Health using Machine Learning Techniques

Dhanasekaran, K; Ramachandran, Rajeswari

doi:10.9790/0661-16348691

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…A study used iterative dichotomiser3(ID3), random forest, and decision tree models to predict the nutritional status of under-five children [ 8 ]. Another Indian study predicted the nutrient effects on human health using machine learning techniques [ 9 ]. So far in our literature search, no published study which used the machine learning model technique to predict under-five mortality was available.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…From a total of 1617 observations, 1132 observations (70% of total observations) were used for training the model, and the remaining 485 observations (30% of total observations) were used for testing or evaluating the model. Various machine-learning algorithms were used to predict child mortality and health service utilization [ 25 , 33 , 34 ]. For this study, the various appropriate machine learning algorithms such as Naïve Bayes, PART, logistic regression, multilayer perceptron, J48, logit Boost, random forest, and AdaBoost were used to predict childhood vaccination among children aged 12–23 months in Ethiopia.…”

Section: Model Buildingmentioning

confidence: 99%

“…Specifically, random forests, logistic regression, J48, logit boost, and Addaboost algorithms were used to predict under-five and neonatal mortality [ 29 , 31 ], undernutrition status of children [ 32 ], and malnutrition among children [ 33 , 34 ]. Additionally, Naïve Bayes and PART algorithms are also used to forecast and classify text documents [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning algorithms’ application to predict childhood vaccination among children aged 12–23 months in Ethiopia: Evidence 2016 Ethiopian Demographic and Health Survey dataset

Demsash,

Chereka,

Walle

et al. 2023

PLoS ONE

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Introduction Childhood vaccination is a cost-effective public health intervention to reduce child mortality and morbidity. But, vaccination coverage remains low, and previous similar studies have not focused on machine learning algorithms to predict childhood vaccination. Therefore, knowledge extraction, association rule formulation, and discovering insights from hidden patterns in vaccination data are limited. Therefore, this study aimed to predict childhood vaccination among children aged 12–23 months using the best machine learning algorithm. Methods A cross-sectional study design with a two-stage sampling technique was used. A total of 1617 samples of living children aged 12–23 months were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 70% and 30% of the observations were used for training, and evaluating the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. All the included algorithms were evaluated using confusion matrix elements. The synthetic minority oversampling technique was used for imbalanced data management. Informational gain value was used to select important attributes to predict childhood vaccination. The If/ then logical association was used to generate rules based on relationships among attributes, and Weka version 3.8.6 software was used to perform all the prediction analyses. Results PART was the first best machine learning algorithm to predict childhood vaccination with 95.53% accuracy. J48, multilayer perceptron, and random forest models were the consecutively best machine learning algorithms to predict childhood vaccination with 89.24%, 87.20%, and 82.37% accuracy, respectively. ANC visits, institutional delivery, health facility visits, higher education, and being rich were the top five attributes to predict childhood vaccination. A total of seven rules were generated that could jointly determine the magnitude of childhood vaccination. Of these, if wealth status = 3 (Rich), adequate ANC visits = 1 (yes), and residency = 2 (Urban), then the probability of childhood vaccination would be 86.73%. Conclusions The PART, J48, multilayer perceptron, and random forest algorithms were important algorithms for predicting childhood vaccination. The findings would provide insight into childhood vaccination and serve as a framework for further studies. Strengthening mothers’ ANC visits, institutional delivery, improving maternal education, and creating income opportunities for mothers could be important interventions to enhance childhood vaccination.

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“…ID3(Iterative Dichotomiser 3), random forest, and decision tree have been used for predicting the nutrition status in under-ve age of children [11]. Another study was conducted in India to predict the nutrient effects on human health using ML techniques [12]. The research paper has been used a machine learning model to predict pre-term birth [13].…”

Section: Page 3/13mentioning

confidence: 99%

Machine Learning Algorithms For Understanding The Determinants of Under-Five Mortality

Saroj

Yadav

Singh

et al. 2021

Preprint

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Background: The death rate of under-five children in India declined last few decades, but few bigger states have poor performance. This is a matter of serious concern for the child's health as well as social development. Nowadays, machine learning techniques play a crucial role in the smart health care system to capture the hidden factors and patterns of outcomes. In this paper, we used machine learning techniques to predict the important factors of under-five mortality.This study aims to explore the importance of machine learning techniques to predict under-five mortality and to find the important factors that cause under-five mortality.The data was taken from the National Family Health Survey-IV of Uttar Pradesh. We used four machine learning techniques like decision tree, support vector machine, random forest, and logistic regression to predict under-five mortality factors and model accuracy of each model. We have also used information gain to rank to know the important variables for accurate predictions in under-five mortality data.Result: Random Forest (RF) predicts the child mortality factors with the highest accuracy of 97.5 %, and the number of living children, births in the last five years, educational level, birth order, total children ever born, currently breastfeeding, and size of child at birth that identifying as essential factors for under-five mortality.Conclusion: The study focuses on machine learning techniques to predict and identify important factors for under-five mortality. The random forest model provides an excellent predictive result for estimating the risk factors of under-five mortality. Based on the resulting outcome, policymakers can make policies and plans to reduce under-five mortality.

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Improved Text Analysis Approach for Predicting Effects of Nutrient on Human Health using Machine Learning Techniques

Abstract: : A text analysis method is introduced which processes the unstructured information from

Cited by 6 publications

References 13 publications

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

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

Machine learning algorithms’ application to predict childhood vaccination among children aged 12–23 months in Ethiopia: Evidence 2016 Ethiopian Demographic and Health Survey dataset

Machine Learning Algorithms For Understanding The Determinants of Under-Five Mortality

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