[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121936-en":3,"doc-seo-121936-105":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},121936,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",7,"Healthcare","A deep learning and machine learning approach to predict neonatal death in the context of São Paulo - Article","Neonatal death remains a critical health challenge, and early identification of at-risk newborns is essential to enable timely care for both child and mother. This work applies machine learning and deep learning models to predict neonatal mortality using historical data from São Paulo. Multiple algorithms are trained and compared, including classical ML methods and sequence/vision-based deep learning architectures. Results show strong predictive performance, with the LSTM achieving the best accuracy among deep learning models.","A deep learning and machine learning approach to predict neonatal death in the context of São Paulo  \nMohon Raihan1, Plabon Kumar Saha1, Rajan Das Gupta2, A Z M Tahmidul Kabir3, Afia Anjum Tamanna4, Md. Harun-Ur-Rashid5, Adnan Bin Abdus Salam1, Md Tanvir Anjum6,  \nA Z M Ahteshamul Kabir7  \n1Department of Computer Science and Engineering, Faculty of Science and Technology, American International University-Bangladesh,  \nDhaka, Bangladesh  \n2Department of Computer Science, Faculty of Mathematical & Physical Sciences, Jahangirnagar University, Dhaka, Bangladesh 3Department of Electrical and Electronic Engineering, Faculty of Engineering, American International University-Bangladesh, Dhaka,  \nBangladesh  \n4Department of Computer Science and Engineering, Faculty of Engineering and Technology, University of Dhaka, Dhaka, Bangladesh 5Department of Computer Science and Engineering, Faculty of Science & Engineering , United International University-Bangladesh,  \nDhaka, Bangladesh  \n6Department of Computer Science and Software Engineering, Faculty of Science and Technology, American International University  \nBangladesh, Dhaka, Bangladesh  \n7Department of Predictive Analytics, Faculty of Science and Engineering, Curtin University, Perth, Australia  \n\n| Article history:\u003Cbr>Received Oct 6, 2022 Revised Jul 9, 2023 Accepted Jul 20, 2023 | Neonatal death is still a concerning reality for underdeveloped and even for some of the developed countries. Worldwide data indicate that 26.693 babies out of 1,000 births according to Macro Trades. To reduce the death early prediction of endangered baby is crucial. An early prediction enables the opportunity to take ample care of the child and mother so that an early child death can be avoided. Machine learning was used to figure out whether a newborn baby is at risk. To train the predictive model historical data of 1.4 million newborn child data was used. Machine learning and deep learning techniques such as Logical regression, K nearest neighbor, Random Forest classifier, Extreme gradient boosting (XGboost), convolutional neural network, long short-term memory (LSTM) . were implemented using the dataset to find out the most robust model which model is the most accurate to identify the mortality of a newborn. From all the machine learning algorithms, the XGboost and random classifier had the best accuracy with 94%, and from the deep learning model, the LSTM had the best outcome with 99% accuracy. Thus, using LSTM of the model shall be best suited to predict whether precaution for a child is necessary.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Child mortality Machine learning Deep learning Mother health Child health |  |\n\nCorresponding Author:  \nA Z M Tahmidul Kabir  \nDepartment of Electrical and Electronic Engineering, American International University-Bangladesh Dhaka-1229, Bangladesh  \nEmail: [tahmidulkabir@gmail.com](tahmidulkabir@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe infant mortality rate (IMR) is the number of children under the age of one who dies for every 1,000 children. Neonatal death is defined as the death of a newborn within the first 28 days (approximately four weeks) after birth. Lack of first aid during pregnancy and after birth is usually blamed for neonatal deaths. In 2022, the current infant mortality rate for the World is 26 ,693 [1] . From the data provided by the world bank [2], it is evident that the mortality rate is extremely high in some places. It especially happens in  \nthe least developed countries like Chad, Nigeria, and Sierra Leone [2] . Infant mortality (IM) is a key indicator of a population's overall health, as well as an approximate predictor of social inequality and financial inequality position. It also shows the availability and quality of healthcare and medical technologies in a certain location. The IMR is used to assess needs and evaluate the efficacy of public interventions.  \nDue to th","cbCaibo27HEfbHSA","https://ap.wps.com/l/cbCaibo27HEfbHSA","pdf",1058274,1,12,"English","en",105,"# Abstract\n# Introduction\n## Infant mortality and neonatal death definitions\n## Data-driven decision making and machine learning\n# Methods and modeling overview\n## Training data from São Paulo\n## Machine learning and deep learning algorithms\n# Results overview\n## Model accuracy comparison\n# Conclusion","[{\"question\":\"Why is early prediction of neonatal death important?\",\"answer\":\"Early prediction helps provide timely care for the newborn and the mother, reducing the chance of avoidable early child death.\"},{\"question\":\"What data source is used to train the predictive models?\",\"answer\":\"The models are trained using historical secondary data on children’s births and deaths in the city of São Paulo.\"},{\"question\":\"Which models performed best for predicting neonatal mortality?\",\"answer\":\"Among machine learning methods, XGBoost and Random Forest showed the best accuracy (reported around 94%). Among deep learning methods, LSTM achieved the best accuracy (reported around 99%).\"}]","A deep learning and machine learning approach to predict neonatal death in the context of São Paulo - Article | PDF",1785807831,30,{"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},"a-deep-learning-and-machine-learning-approach-to-predict-neonatal-death-in-the-context-of-sao-paulo-article","",{"@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/a-deep-learning-and-machine-learning-approach-to-predict-neonatal-death-in-the-context-of-sao-paulo-article/121936/",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","2026-08-04",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},"Why is early prediction of neonatal death important?","Question",{"text":75,"@type":76},"Early prediction helps provide timely care for the newborn and the mother, reducing the chance of avoidable early child death.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data source is used to train the predictive models?",{"text":80,"@type":76},"The models are trained using historical secondary data on children’s births and deaths in the city of São Paulo.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models performed best for predicting neonatal mortality?",{"text":84,"@type":76},"Among machine learning methods, XGBoost and Random Forest showed the best accuracy (reported around 94%). 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