[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120523-en":3,"doc-seo-120523-105":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},120523,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",7,"Healthcare","Unveiling the Unborn - Advancing Fetal Health Classification through Machine Learning - Original Research","Fetal health classification is a critical obstetrics task for early identification and management of potential problems, yet it is hindered by complex data and limited labeled samples. The study proposes a machine-learning approach using a LightGBM classifier trained on a comprehensive dataset. The model reaches 98.31% accuracy on a test set and combines fetal heart rate, uterine contractions, and maternal blood pressure to support a more objective, accurate evaluation. Comprehensive feature selection and assessment underpin the method’s novelty, aiming to improve early detection and clinical outcomes, with future validation on larger datasets and clinical application development.","Artificial Intelligence in Health  \n*Corresponding author:  \nSujith K. Mandala ([sujithkmandala15@gmail.com](sujithkmandala15@gmail.com)) Citation: Mandala SK, 2024, Unveiling the unborn: Advancing  \nfetal health classification through machine learning. Artif Intell Health, 1(1): 2121.  \n[https://doi.org/10.36922/aih.2121](https://doi.org/10.36922/aih.2121)  \n[Received:](Received: October 26)[ October 26](Received: October 26) , 2023  \nAccepted: December 20, 2023  \nPublished Online: December 26, 2023  \nCopyright: © 2024 Author(s) . This is an Open-Access article distributed under the terms of the Creative Commons Attribution License, permitting distribution, and reproduction in any medium, provided the original work is properly cited.  \nPublisher’s Note: AccScience Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nORIGINAL RESEARCH ARTICLE  \nUnveiling the unborn: Advancing fetal health classification through machine learning  \nSujith K. Mandala*  \nDepartment of Information Technology, St. Martin’s Engineering College, Hyderabad, Telangana, India  \nAbstract  \nFetal health classification is a critical task in obstetrics, which enables early identification and management of potential health problems. However, it remains a challenging task due to data complexity and limited labeled samples. This research paper presents a novel machine-learning approach for fetal health classification, leveraging a LightGBM classifier trained on a comprehensive dataset. The proposed model achieves an impressive accuracy of 98.31% on a test set. The findings demonstrate machine learning can potentially enhance fetal health classification, offering a more objective and accurate assessment. Notably, the presented approach combines various features, such as fetal heart rate, uterine contractions, and maternal blood pressure, to provide a comprehensive evaluation. This methodology holds promise for improving early detection and treatment of fetal health issues, ensuring better outcomes for both mothers and babies. In addition to the high accuracy, the novelty of this approach lies in its comprehensive feature selection and assessment methodology. By incorporating multiple data points, this model offers a more holistic and reliable evaluation compared to traditional methods. This research has significant implications in the field of obstetrics, paving the way for advancements in early detection and intervention of fetal health concerns. Future work involves validating the model on a larger dataset and developing a clinical application. Ultimately, we anticipate that our research will revolutionize the assessment and management of fetal health, contributing to improved healthcare outcomes for expectant mothersand their fetuses.  \nKeywords: LightGBM; Fetal health; Machine learning; Cardiotocography; Artificial intelligence  \n1. Introduction  \nFetal health classification is a critical task in obstetrics, as it can help identify and manage fetal health problems at early stage. Accurate assessment of fetal health is crucial for timely intervention and improved health-care outcomes for both mothers and their babies. Traditional methods of fetal health assessment rely on subjective interpretations and limited sets of features, which may lead to inconsistent results and delayed interventions. In recent years, machine learning (ML) techniques have emerged as powerful tools for medical data analysis and classification tasks. ML models have the potential to provide a more objective and accurate assessment regarding the fetal health by leveraging a wide range of data points and complex patterns. Through learning from vast amounts of  \ndata, these models can capture intricate relationships and patterns that may not be apparent to human observers.  \nThis research aims to address the challenges associated with fetal health classification using ML models, by proposing a novel approach that improves the ac","cbCailytDfL124Sw","https://ap.wps.com/l/cbCailytDfL124Sw","pdf",1143295,1,11,"English","en",105,"# Abstract\n# Introduction\n# Related work and existing methods","[{\"question\":\"Why is fetal health classification challenging in obstetrics?\",\"answer\":\"It is difficult due to data complexity and limited labeled samples, which can reduce reliability and delay intervention.\"},{\"question\":\"What machine-learning model is used for fetal health classification?\",\"answer\":\"The paper uses a LightGBM classifier trained on a comprehensive dataset.\"},{\"question\":\"Which fetal and maternal features does the proposed approach use?\",\"answer\":\"It combines fetal heart rate, uterine contractions, and maternal blood pressure to provide a comprehensive evaluation.\"}]","Unveiling the Unborn - 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