[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119907-en":3,"doc-seo-119907-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119907,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",7,"Healthcare","Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data","Persistent efforts to reduce childhood mortality drive demand for reliable medical benchmarks. With global under-5 mortality remaining around five million and many deaths preventable, machine learning has become a key approach for evaluating fetal health. This work compares classification performance of SVM, random forest, and TabNet, and applies dimensionality reduction with PCA and LDA to improve accuracy using fewer features. A TabNet model achieves 94.36% classification accuracy, supporting precise fetal health classification and feature importance discovery.","Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data  \nBinod Regmi  \nDepartment of Physics and Astronomy Mississippi state university Mississippi State, MS 39762, USA [regmibinod53@gmail.com](regmibinod53@gmail.com)  \nChiranjibi Shah  \nDepartment of Electrical and Computer Engineering Mississippi State University Mississippi State, MS 39762, USA [chiranjibishahmsu@gmail.com](chiranjibishahmsu@gmail.com)  \narXiv :2311 . 10962v1 [ cs .LG] 18 Nov 2023  \nAbstract—The persistent battle to decrease childhood mortality serves as a commonly employed benchmark for gauging advancements in the field of medicine. Globally, the under- 5 mortality rate stands at approximately 5 million, with a significant portion of these deaths being avoidable. Given the significance of this problem, Machine learning-based techniques have emerged as a prominent tool for assessing fetal health. In this work, we have analyzed the classification performance of various machine learning models for fetal health analysis. Classification performance of various machine learning models, such as support vector machine (SVM), random forest(RF), and attentive interpretable tabular learning (TabNet) have been assessed on fetal health. Moreover, dimensionality reduction techniques, such as Principal component analysis (PCA) and Linear discriminant analysis (LDA) have been implemented to obtain better classification performance with less number of features. A TabNet model on a fetal health dataset provides a classification accuracy of 94.36% . In general, this technology empowers doctors and healthcare experts to achieve precise fetal health classification and identify the most influential features in the process.  \nIndex Terms—Fetal health, Machine learning, principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), attentive interpretable tabular learning (TabNet).  \nI. INTRODUCTION  \nPremature birth (PTB) represents a significant public health concern with far-reaching implications for both individuals and communities [1] . This phenomenon is characterized by its distinct nature, contributing to adverse outcomes for both families and society. Globally, neonatal mortality and morbidity rank as the primary contributors to infant fatalities and illnesses, making them the second most prevalent cause of infant mortality in developing nations. Pregnancy and childbirth have provided opportunities for medical interventions, prompting professionals and scholars to explore various successful approaches to reduce the incidence of premature births and complications among expectant mothers. Healthcare services play a crucial role in these endeavors, with preventive measures offered to all pregnant women to mitigate the risk of preterm birth and other medical issues. Interventions focus on enhancing women’s awareness of early pregnancy symptoms that may indicate potential difficulties. Maternal history is a vital aspect of the examination process for pregnant  \nwomen, while neonatal research investigates specific therapeutic interventions for newborns. Assessing the health, illnesses, and care provided to newborns is an integral part of this research. For several decades, infant mortality has remained a persistent concern within healthcare systems worldwide. While advancements have been made in developing tools to evaluate various aspects of fetal well-being, the interpretation of cardiotocography (CTG) data can pose challenges, particularly in regions lacking expert obstetricians [2] . Even in areas with access to medical professionals, the process of individually diagnosing fetuses based on CTG measurements can be timeconsuming and generally inefficient. However, the application of machine learning models allows for fetal health classifications to be made without the presence of obstetricians and in a more efficient manner. These models have demonstrated high accuracy in their predicti","cbCaikSGlTx4qR0O","https://ap.wps.com/l/cbCaikSGlTx4qR0O","pdf",510412,1,5,"English","en",105,"# Abstract\n# Index Terms\n# Introduction\n## Premature birth and fetal health assessment challenges\n## Explainable machine learning for decision support\n## Models and techniques overview (SVM, RF, TabNet; PCA, LDA)","[{\"question\":\"Which machine learning models are evaluated for fetal health classification?\",\"answer\":\"The document evaluates support vector machine (SVM), random forest (RF), and attentive interpretable tabular learning (TabNet) on fetal health data.\"},{\"question\":\"How do PCA and LDA contribute to model performance?\",\"answer\":\"Principal component analysis (PCA) and linear discriminant analysis (LDA) are used to reduce dimensionality, aiming to improve classification accuracy while using fewer features.\"},{\"question\":\"What classification accuracy is reported for the TabNet model?\",\"answer\":\"The TabNet model on a fetal health dataset achieves a classification accuracy of 94.36%.\"}]","Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data | 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machine learning models are evaluated for fetal health classification?","Question",{"text":76,"@type":77},"The document evaluates support vector machine (SVM), random forest (RF), and attentive interpretable tabular learning (TabNet) on fetal health data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do PCA and LDA contribute to model performance?",{"text":81,"@type":77},"Principal component analysis (PCA) and linear discriminant analysis (LDA) are used to reduce dimensionality, aiming to improve classification accuracy while using fewer features.",{"name":83,"@type":74,"acceptedAnswer":84},"What classification accuracy is reported for the TabNet model?",{"text":85,"@type":77},"The TabNet model on a fetal health dataset achieves a classification accuracy of 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