[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117408-en":3,"doc-seo-117408-105":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},117408,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Machine Learning-Based Prediction of First Trimester Down Syndrome Risk in East Asian Populations","Machine learning methods are applied to predict first-trimester Down syndrome risk in East Asian populations, addressing the limited evidence for this screening stage. Data from a Down syndrome screening database at Taipei Chang Gung Memorial Hospital were analyzed from May 1, 2018, to February 29, 2024, using 3,812 cases and 14 clinical features. Twelve models were assessed with AUC, accuracy, precision, recall, and F1, with ROS and RUS balancing to improve performance. An ANN model combined with ROS achieved an AUC of 0.939 and accuracy of 0.97, supporting efficient screening.","Risk Management and Healthcare Policy downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nRisk Management and Healthcare Policy  \nOpen Access Full Text Article ORIGINAL RESEARCH  \nMachine Learning-Based Prediction of First Trimester Down Syndrome Risk in East Asian Populations  \nYen-Tin Chen 1 , 2 , Gina Jinna Chen 3 , Yu-Shiang Lin 1  \n1In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; 2Department of Obstetrics and Gynecology, Taipei Chang Gung Memorial Hospital, Taipei, Taiwan; 3Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, People’s Republic of China  \nCorrespondence: Yu-Shiang Lin, In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, No. 250, Wuxing St., Xinyi Dist., Taipei City, 110, Taiwan, Email [eriklin@tmu.edu.tw](eriklin@tmu.edu.tw)  \n\n| Purpose: Down syndrome is the most common chromosomal abnormality in newborns, often leading to developmental delays and congenital structural anomalies. This study employed multiple machine learning models to perform risk prediction and result exploration for first-trimester Down syndrome in East Asian populations, aiming to identify an optimal risk prediction model that will enhance future predictions of Down syndrome risk and improve the efficiency of the screening process.\u003Cbr>Patients and Methods: This study collected data from the Down syndrome screening database at Taipei Chang Gung Memorial Hospital from May 1, 2018, to February 29, 2024. The dataset included 3,812 cases available for analysis, comprising 165 high-risk cases and 3,647 low-risk cases. Fourteen features (including maternal age, nuchal translucency thickness, serum markers, etc.) were input into the twelve machine learning models, along with seven data-balancing algorithms, to explore the risk prediction outcomes. The performance of these models was thoroughly evaluated using AUC (Area Under the Curve), accuracy, precision, recall, and F1 scores. Results: Among the twelve machine learning models, the highest recall of 0.84 for high-risk cases was achieved by LightGBM combined with the RUS (Random Undersampling) data balancing algorithm. The highest AUC of 0.939 was attained by the ANN and LSTM models when combined with the ROS (Random Oversampling) data balancing algorithm.\u003Cbr>Conclusion: The proposed ANN machine learning model, based on deep neural networks and combined with the ROS data balancing method, achieved an impressive AUC of 0.939 for classifying first-trimester Down syndrome risk in the East Asian population. Notably, this model also achieved an outstanding classification accuracy of 0.97. These results demonstrate the potential of the proposed ANN machine learning model for the accurate prediction of first-trimester Down syndrome risk.\u003Cbr>Keywords: machine learning, first trimester down syndrome screening, deep neural network |\n| --- |\n| Introduction\u003Cbr>Down syndrome is a genetic disorder caused mainly by an extra chromosome 21. It is one of the most common chromosomal abnormalities in newborns. In addition to mild to moderate developmental delays, 1–4 individuals with Down syndrome may also exhibit specific congenital structural abnormalities, such as congenital heart disease (eg, ventricular septal defect),5 gastrointestinal abnormalities,6 and hypotonia (low muscle tone) .4 In Taiwan, the incidence of Down syndrome is approximately 7.92 per 10,000 live births,7 meaning that about one in every 1,263 newborns is affected. Caring for individuals with Down syndrome requires significant effort and social resources, and it has a profound impact on their mothers and families.\u003Cbr>The development of machine learning has made significant progress in recent years, particularly in its applications in medicine, such as image analysis,8 cancer diagnosis,9–11 diagn","cbCainzgNlNUqJ83","https://ap.wps.com/l/cbCainzgNlNUqJ83","pdf",4586901,1,12,"English","en",105,"# Introduction\n## Background on Down syndrome\n## Role and limits of machine learning in first-trimester screening","[{\"question\":\"What is the primary purpose of the study?\",\"answer\":\"To use multiple machine learning models to predict first-trimester Down syndrome risk in East Asian populations and identify an optimal model to improve screening efficiency.\"},{\"question\":\"How was the dataset collected and how many cases were included?\",\"answer\":\"Cases were collected from the Down syndrome screening database at Taipei Chang Gung Memorial Hospital from May 1, 2018, to February 29, 2024, for a total of 3,812 cases (165 high-risk and 3,647 low-risk).\"},{\"question\":\"Which model and data balancing approach performed best?\",\"answer\":\"An ANN deep learning model combined with ROS (random oversampling) achieved the highest AUC of 0.939 and classification accuracy of 0.97 for first-trimester risk classification.\"}]","Machine Learning-Based Prediction of First Trimester Down Syndrome Risk in East Asian Populations | 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is the primary purpose of the study?","Question",{"text":75,"@type":76},"To use multiple machine learning models to predict first-trimester Down syndrome risk in East Asian populations and identify an optimal model to improve screening efficiency.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset collected and how many cases were included?",{"text":80,"@type":76},"Cases were collected from the Down syndrome screening database at Taipei Chang Gung Memorial Hospital from May 1, 2018, to February 29, 2024, for a total of 3,812 cases (165 high-risk and 3,647 low-risk).",{"name":82,"@type":73,"acceptedAnswer":83},"Which model and data balancing approach performed best?",{"text":84,"@type":76},"An ANN deep learning model combined with ROS (random oversampling) achieved the highest AUC of 0.939 and classification accuracy of 0.97 for first-trimester risk 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