[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122496-en":3,"doc-seo-122496-105":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},122496,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Machine Learning-Based Maternal Health Risk Assessment - A Comparative Analysis of Classification Algorithms","Maternal health risk assessment remains a critical challenge, especially in resource-limited settings where early detection of high-risk pregnancies can improve maternal and fetal outcomes. This study compares multiple machine learning algorithms for predicting maternal health risk levels using physiological parameters. The analysis uses a dataset of 1014 pregnant women and evaluates feature-driven classification across mild, moderate, and severe risk categories. Results highlight strong performance for Random Forest and SVM, with additional discussion of model suitability and the need for broader validation.","Machine Learning-Based Maternal Health Risk Assessment: A Comparative Analysis of Classification Algorithms for Predicting Risk Levels During Pregnancy  \nUsha Adiga 1* , Sampara Vasishta 1 , P. Supriya 1 , P. Peddareddemma 1 , Lokesh Ravi2  \n1Department of Biochemistry, Apollo Institute of Medical Sciences and Research Chittoor, Murukambattu - 517127, Chittoor, Andhra Pradesh, India  \n2 Centre for Digital Health & Precision Medicine, The Apollo University, Chittoor, Andhra Pradesh, 517127, India  \nAbstract: Background: Maternal health risk assessment remains a critical challenge in healthcare, particularly in resource-limited settings where early identification of high-risk pregnancies can significantly impact maternal and fetal outcomes. This study evaluates the performance of multiple machine learning algorithms for predicting maternal health risk levels using physiological parameters.  \nMethods: We analyzed a dataset of 1014 pregnant women from Kaggle, incorporating six key features: age, systolic blood pressure, diastolic blood pressure, blood sugar levels, body temperature, and heart rate. Risk levels were classified as mild (0), moderate (1), and severe (2) . Four machine learning algorithms were implemented and compared:  \nLogistic Regression, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) .  \nResults: Random Forest and SVM achieved perfect classification performance with 100% accuracy, precision, recall, and F1-scores across all risk categories. Logistic Regression demonstrated strong performance with 98% overall accuracy, showing minor challenges in recall for moderate risk cases (93%) . KNN achieved 98% accuracy with balanced performance across risk categories, though slightly lower precision for mild risk cases (95%) .  \nConclusion: Machine learning algorithms, including Random Forest and SVM, show promise in predicting maternal health risks; however, further validation across diverse populations is essential before clinical adoption.  \nKeywords: Maternal health, risk prediction, machine learning, pregnancy complications, healthcare analytics.  \nINTRODUCTION  \nMaternal health represents one of the most critical public health challenges globally, with approximately 295,000 women dying from pregnancy-related causes each year according to the World Health Organization [1] . The burden of maternal mortality and morbidity disproportionately affects developing countries, where access to quality healthcare services remains limited and early risk identification systems are often inadequate [2] . The complexity of maternal health assessment stems from the multifaceted nature of pregnancy-related complications, which can arise from various physiological, social, and environmental factors that interact in unpredictable ways [3] .  \nTraditional approaches to maternal health risk assessment rely heavily on clinical expertise and standardized protocols that may not adequately capture the subtle patterns and interactions between multiple risk factors [4] . Healthcare professionals often face challenges in processing and interpreting multiple  \n*Address correspondence to this author at the Department of Biochemistry, Apollo Institute of Medical Sciences and Research Chittoor, Murukambattu - 517127, Chittoor, Andhra Pradesh, India; Tel: 8277781638;  \nE-mail: [ushachidu@aimsrchittoor.edu.in](ushachidu@aimsrchittoor.edu.in)  \nphysiological parameters simultaneously, particularly in high-volume clinical settings where time constraints and resource limitations can compromise the quality of risk assessment [5] . This limitation becomes more pronounced in rural and underserved areas where specialized obstetric expertise may not be readily available [6] .  \nThe advent of artificial intelligence and machine learning technologies has opened new avenues for enhancing healthcare delivery and decision-making processes [7] . Machine learning algorithms possess the unique capability to identify complex patterns and rel","cbCaipfPftobVoNy","https://ap.wps.com/l/cbCaipfPftobVoNy","pdf",311603,1,7,"English","en",105,"# Introduction\n## Maternal health challenges and risk identification gaps\n## Role of machine learning in healthcare decision-making\n# Objectives\n## Comparative evaluation of classification algorithms","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To evaluate and compare the performance of four machine learning algorithms in predicting maternal health risk levels using physiological parameters collected during pregnancy.\"},{\"question\":\"Which features and dataset were used for risk prediction?\",\"answer\":\"The study analyzed a dataset of 1014 pregnant women and used six features: age, systolic blood pressure, diastolic blood pressure, blood sugar levels, body temperature, and heart rate.\"},{\"question\":\"How did the compared algorithms perform?\",\"answer\":\"Random Forest and SVM achieved perfect classification performance with 100% accuracy, precision, recall, and F1-scores across all risk categories. Logistic Regression showed 98% overall accuracy, while KNN also achieved 98% accuracy with balanced performance across categories.\"}]","Machine Learning-Based Maternal Health Risk Assessment - A Comparative Analysis of Classification Algorithms | PDF",1785810949,18,{"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-based-maternal-health-risk-assessment-a-comparative-analysis-of-classification-algorithms","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-maternal-health-risk-assessment-a-comparative-analysis-of-classification-algorithms/122496/",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},"What is the main goal of this study?","Question",{"text":75,"@type":76},"To evaluate and compare the performance of four machine learning algorithms in predicting maternal health risk levels using physiological parameters collected during pregnancy.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which features and dataset were used for risk prediction?",{"text":80,"@type":76},"The study analyzed a dataset of 1014 pregnant women and used six features: age, systolic blood pressure, diastolic blood pressure, blood sugar levels, body temperature, and heart rate.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the compared algorithms perform?",{"text":84,"@type":76},"Random Forest and SVM achieved perfect classification performance with 100% accuracy, precision, recall, and F1-scores across all risk categories. Logistic Regression showed 98% overall accuracy, while KNN also achieved 98% accuracy with balanced performance across categories.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]