Feature selection and prediction of small-for-gestational-age infants

Li, Jianqiang; Liu, Lu; Zhou, Meng Chu; Yang, Ji Jiang; Shi, Chen; Liu, Hui Ting; Wang, Qing; Pan, Hui; Sun, Zhoutong; Tan, Feng

doi:10.1007/s12652-018-0892-2

Cited by 15 publications

(3 citation statements)

References 33 publications

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“…So there is a need for early and accurate prediction of preterm infants for appropriate clinical intervention. Li et al [31] have made a comparison of five feature selection methods, namely the chi-square test, information gain, minimum redundancy maximum relevance (MRMR), stepwise logistic regression, and Gini index in RF, to identify the risk factors for SGA infants. They have evaluated their work by applying 4 classifiers (LR, NB, SVM, and RF) while taking precision and AUC as evaluation criteria.…”

Section: Discussionmentioning

confidence: 99%

Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches

et al. 2022

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The rate of infant mortality (IMR) in a population under one year of age is a marker for infant mortality. It is a major sensitive marker of a community's overall physical health. Protecting the lives of newborns has become a challenging issue in public health, development programs, and humanitarian initiatives. Almost 10.1% infants died in the United States of America (USA) in 2021. Therefore, this paper aims to extract and understand the various influential factors causing infant deaths in the USA. A crowding distance-based multi-objective ant lion optimization (MOALO-CD) is proposed here with statistical evidence for feature selection. The proposed technique is compared with competitive metaheuristic models such as multi-objective genetic algorithm based on crowding distance (MOGA-CD), multi-objective filter approaches, and recursive feature elimination. Various machine learning classifiers are applied to the selected feature subset obtained from MOALO-CD on the USA's infant dataset. Extensive experimental results indicate that the proposed model outperforms the existing metaheuristic approaches in terms of Generational Distance, Inverted Generational Distance, Spread, and Hyper volume. Also, the comparative analysis of various machine learning models reveals that random forest achieves significantly better performance on the feature subset obtained from MOALO-CD.

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Section: Discussionmentioning

confidence: 99%

Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches

et al. 2022

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“…Irrelevant and unnecessary features not only affect classifier performance but also demand excessive computational resources and time for the classification task [28][29][30][31]. A variety of feature selection, extraction, and reduction schemes are proposed by various researchers to deal with the curse of irrelevant and dimensionality problem of a classification system [23,28,[32][33][34]. In this article, we recommend using an ensemble of feature selection and extraction schemes to build an accurate and state of the art LGA prediction model.…”

Section: Preparation Of the Principal Feature Vectormentioning

confidence: 99%

Diagnosis and Prediction of Large-for-Gestational-Age Fetus Using the Stacked Generalization Method

Akhtar

Pei

et al. 2019

Applied Sciences

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An accurate and efficient Large-for-Gestational-Age (LGA) classification system isdeveloped to classify a fetus as LGA or non-LGA, which has the potential to assist paediatricians andexperts in establishing a state-of-the-art LGA prognosis process. The performance of the proposedscheme is validated by using LGA dataset collected from the National Pre-Pregnancy and ExaminationProgram of China (2010–2013). A master feature vector is created to establish primarily datapre-processing, which includes a features’ discretization process and the entertainment of missingvalues and data imbalance issues. A principal feature vector is formed using GridSearch-basedRecursive Feature Elimination with Cross-Validation (RFECV) + Information Gain (IG) featureselection scheme followed by stacking to select, rank, and extract significant features from the LGAdataset. Based on the proposed scheme, different features subset are identified and provided tofour different machine learning (ML) classifiers. The proposed GridSearch-based RFECV+IG featureselection scheme with stacking using SVM (linear kernel) best suits the said classification processfollowed by SVM (RBF kernel) and LR classifiers. The Decision Tree (DT) classifier is not suggestedbecause of its low performance. The highest prediction precision, recall, accuracy, Area Underthe Curve (AUC), specificity, and F1 scores of 0.92, 0.87, 0.92, 0.95, 0.95, and 0.89 are achievedwith SVM (linear kernel) classifier using top ten principal features subset, which is, in fact higherthan the baselines methods. Moreover, almost every classification scheme best performed with tenprincipal feature subsets. Therefore, the proposed scheme has the potential to establish an efficientLGA prognosis process using gestational parameters, which can assist paediatricians and experts toimprove the health of a newborn using computer aided-diagnostic system.

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“…However, machine learning (ML) techniques, which could handle complicated relationships and optimize prediction performance from complicated dataset, can overcome these restrictions (16,17). As for predicting the risk of SGA, in a few studies, ML classifiers were used to develop SGA prediction tools in the overall population (18)(19)(20)(21)(22). Unfortunately, the prediction tools did not perform well, with a maximum area under the receiver operating characteristic (ROC) curve (AUC) of 0.7+.…”

Section: Introductionmentioning

confidence: 99%

Predictive models for small-for-gestational-age births in women exposed to pesticides before pregnancy based on multiple machine learning algorithms

Bai

Zhou

et al. 2022

Front. Public Health

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BackgroundThe association between prenatal pesticide exposures and a higher incidence of small-for-gestational-age (SGA) births has been reported. No prediction model has been developed for SGA neonates in pregnant women exposed to pesticides prior to pregnancy.MethodsA retrospective cohort study was conducted using information from the National Free Preconception Health Examination Project between 2010 and 2012. A development set (n = 606) and a validation set (n = 151) of the dataset were split at random. Traditional logistic regression (LR) method and six machine learning classifiers were used to develop prediction models for SGA neonates. The Shapley Additive Explanation (SHAP) model was applied to determine the most influential variables that contributed to the outcome of the prediction.Results757 neonates in total were analyzed. SGA occurred in 12.9% (n = 98) of cases overall. With an area under the receiver-operating-characteristic curve (AUC) of 0.855 [95% confidence interval (CI): 0.752–0.959], the model based on category boosting (CatBoost) algorithm obtained the best performance in the validation set. With the exception of the LR model (AUC: 0.691, 95% CI: 0.554–0.828), all models had good AUCs. Using recursive feature elimination (RFE) approach to perform the feature selection, we included 15 variables in the final model based on CatBoost classifier, achieving the AUC of 0.811 (95% CI: 0.675–0.947).ConclusionsMachine learning algorithms can develop satisfactory tools for SGA prediction in mothers exposed to pesticides prior to pregnancy, which might become a tool to predict SGA neonates in the high-risk population.

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Feature selection and prediction of small-for-gestational-age infants

Cited by 15 publications

References 33 publications

Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches

Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches

Diagnosis and Prediction of Large-for-Gestational-Age Fetus Using the Stacked Generalization Method

Predictive models for small-for-gestational-age births in women exposed to pesticides before pregnancy based on multiple machine learning algorithms

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