ROC curves of ML model for predicting LBW in the test dataset: (a) Logistic Regression, (b) Random Forest, (c) Decision Tree, (d) Artificial Neural Network, (e) Extreme Gradient Boosting and (f) Light Gradient Boosting Machine.

ROC curves of ML model for predicting LBW in the test dataset: (a) Logistic Regression, (b) Random Forest, (c) Decision Tree, (d) Artificial Neural Network, (e) Extreme Gradient Boosting and (f) Light Gradient Boosting Machine.

<p>ROC curves of ML model for predicting LBW in the test dataset: (a) Logistic Regression, (b) Random Forest, (c) Decision Tree, (d) Artificial Neural Network, (e) Extreme Gradient Boosting and (f) Light Gradient Boosting Machine.</p>

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Bibliographic Details
Main Author:	Nourin Sultana (22339463) (author)
Other Authors:	Zeba Afia (22339466) (author), Isteaq Kabir Sifat (20469226) (author), Shamsuz Zoha (22339469) (author), Tajin Ahmed Jisa (22339472) (author), Md. Kaderi Kibria (14662982) (author)
Published:	2025
Subjects:	Medicine Biotechnology Science Policy Computational Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified increased infant mortality including logistic regression evaluates machine learning 192 complete records term health complications key risk factors associated risk factors shap analysis revealed lr ), rf low birth weight evaluated using accuracy div >< p lbw risk followed ‘ division ’ xgb model outperformed reducing lbw prevalence six ml algorithms risk factors model ’ xgb ), shap values roc analysis rf ). health survey ‘ place ‘ marriage section ’ model performance predicting lbw predict lbw twelve features strongest predictors random forest individual features income countries highest performance findings demonstrate f </ elastic net effective tool delivery ’. decision tree based selection 8 %.
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