[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119096-en":3,"doc-seo-119096-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119096,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",7,"Healthcare","Machine Learning Approach For Early Prediction Of Low Birth Weight Cases","Predicting birth weight plays a key role in prenatal care by enabling earlier intervention and more personalized health planning for pregnant mothers and their infants. The project presents a “Birth Weight Predictor” web application built with Flask and powered by machine learning to estimate birth weight from maternal characteristics. It uses a dataset covering factors such as age, weight, height, medical history, and habits. A voting classifier with Random Forest, boosting algorithms, and logistic regression analyze the inputs to uncover predictive patterns. The tool supports healthcare providers, expectant parents, and researchers by improving early insight, reducing prenatal risks, and promoting wellbeing.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 45 IssueS-2 Year 2024 Page 157-164  \nMachine Learning Approach For Early Prediction Of Low Birth Weight Cases  \nD.E.Gnana Shiney1, K.Anitha2, L.Hari Chandana3, K.Meghana4, J.Durga Prasad5  \n1*M. Tech., Assistant Professor, Information Technology, Seshadri Rao Gudlavalleru Engineering College.  \n2,3,4,5Students, Information Technology, Seshadri Rao Gudlavalleru Engineering College.  \n*Corresponding Author: D.E.Gnana Shiney  \n*M. Tech., Assistant Professor, Information Technology, Seshadri Rao Gudlavalleru Engineering College.  \n\n| C License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>Predicting baby birth weight is an important component of prenatal care since it allows for early intervention and personalised healthcare for pregnant mothers and their infants. This project introduces \"Birth Weight Predictor,\" a user-friendly web application developed using Flask and powered by machine learning that estimates birth weight depending on maternal characteristics. The application makes use of a large dataset that includes maternal health variables such as age, weight, height, medical history, habits, and more. Machine learning methods are used to analyse these maternal characteristics and predict birth weight accurately. The following are some of the application's key features: Maternal Data Input: Users can enter maternal data such as age, weight, height, and other pertinent parameters to get a personalised birth weight prediction Sophisticated machine learning methods, such as voting classifier with Random forest, Boosting algorithm, and logistic regression, are used to process the maternal data, allowing the model to uncover relevant patterns and associations for accurate predictions. Flask Web Interface: The user-friendly Flask-based web interface makes birth weight projections accessible and instructive for both healthcare providers and pregnant parents. This software is a useful tool for healthcare providers, expectant parents, and researchers, as it provides early insights into prospective birth weight outcomes. It contributes to improving prenatal care, minimising problems, and maintaining the well-being of both moms and newborns by leveraging the power of machine learning and Flask. |\n| --- | --- |\n\n1. INTRODUCTION  \nBirth weight affects its possibilities of endurance. Low birth weight (LBW) is turning out to be more an issue, especially in arising nations. A significant reason for neonatal passing is low birth weight, under 2500 g. Infants brought into the world at a low birth weight are multiple times bound to bite the dust than children brought into the world at a typical birth weight  \nIt's likewise a decent mark of a kid's future unexpected issues. Low birth weight influences one out of each and every seven infants, representing around 14.6 percent of the children conceived around the world. Anticipating birth weight is a critical part of pre-birth care and has a few significant ramifications for both maternal and neonatal wellbeing. Proof shows that the worldwide commonness of LBW dropped by 1.2 percent every year somewhere in the range of 2000 and 2015, implying that progress is deficient to satisfy the World Wellbeing Gathering's low birth weight focus of 30% by 2025 [1] . LBW is as yet a serious general wellbeing worry across the world [1], putting children and infants at an expanded gamble of death and  \ndismalness. Thus, one of the principal points of the 'A World Fit for Kids' drive is to diminish low birth weight as a critical commitment to the Thousand years Improvement Objective.  \nBirth weight (BW) assumes a significant part in the endurance and wellbeing of babies, and precise BW expectation will assist medical services specialists with pursuing ideal choices. Babies with a BW of ≤ 2500 g are considered as low BW (LBW) newborn children. Low BW in newborn children can happen as a result of different reasons like maternal eating regimen, close pregnancy stretches, co","cbCaiuxrqJkRhO2n","https://ap.wps.com/l/cbCaiuxrqJkRhO2n","pdf",439568,1,8,"English","en",105,"# Introduction\n## Problem and importance of low birth weight\n## Need for accurate prenatal prediction\n## Challenges for ML-based clinical systems\n## Dataset limitations and feature selection approaches","[{\"question\":\"What does the “Birth Weight Predictor” application estimate and how?\",\"answer\":\"It estimates birth weight for prenatal planning by using maternal input data. The application is a Flask-based web interface powered by machine learning models to generate predictions.\"},{\"question\":\"Which maternal factors are used as inputs for prediction?\",\"answer\":\"The method uses variables such as maternal age, weight, height, medical history, habits, and related parameters from the dataset.\"},{\"question\":\"Why are machine learning methods considered useful for birth weight assessment?\",\"answer\":\"ML approaches can analyze maternal characteristics to support accurate estimation and classification. The document also notes ML as a common choice for clinical decision support, while highlighting challenges like limited dataset availability and missing records.\"}]","Machine Learning Approach For Early Prediction Of Low Birth Weight Cases | PDF",1785722368,20,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-approach-for-early-prediction-of-low-birth-weight-cases","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-approach-for-early-prediction-of-low-birth-weight-cases/119096/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the “Birth Weight Predictor” application estimate and how?","Question",{"text":75,"@type":76},"It estimates birth weight for prenatal planning by using maternal input data. The application is a Flask-based web interface powered by machine learning models to generate predictions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which maternal factors are used as inputs for prediction?",{"text":80,"@type":76},"The method uses variables such as maternal age, weight, height, medical history, habits, and related parameters from the dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"Why are machine learning methods considered useful for birth weight assessment?",{"text":84,"@type":76},"ML approaches can analyze maternal characteristics to support accurate estimation and classification. 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