[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120191-en":3,"doc-seo-120191-105":30,"detail-sidebar-cat-0-en-105":90},{"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},120191,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","Thrombophilia Prediction Using Machine Learning Algorithms","Thrombophilia in pregnancy arises from intertwined inherited and acquired factors that elevate blood coagulation and can lead to placental ischemic conditions. Early detection of pregnancy-related thrombophilia risk is essential for timely preventive measures and personalized therapy. This study introduces a machine learning approach, emphasizing neural networks, using demographic, lifestyle, and clinical data from 35 pregnant women (22 healthy, 13 with thrombophilia). Models were trained and evaluated with cross-validation and performance metrics, and results demonstrate strong predictive effectiveness of decision trees and neural networks.","2nd International Conference on Chemo and Bioinformatics,  \nSeptember 28-29, 2023. Kragujevac, Serbia  \nThrombophilia Prediction Using Machine Learning Algorithms  \nAldina R. Avdić1, Natasa Z. Djordjević2, Ulfeta A. Marovac1*, Lejlija M. Memić1, Zana Ć. Dolićanin3, Goran M. Babić4  \n1 State University of Novi Pazar, Department of Technical and Technological Sciences, Vuka Karadžića 9, 36300 Novi Pazar, Serbia, e-mail: [apljaskovic@np.ac.rs](apljaskovic@np.ac.rs), [umarovac@np.ac.rs](umarovac@np.ac.rs), [lmemic@np.ac.rs](lmemic@np.ac.rs)  \n2 State University of Novi Pazar, Department of Natural and Mathematical Sciences, Vuka Karadžića 9, 36300 Novi Pazar, Serbia, [e-mail: natasadj@np.ac.rs](e-mail: natasadj@np.ac.rs)  \n3 State University of Novi Pazar, Department of Biomedical Sciences, Vuka Karadžića 9, 36300 Novi Pazar, Serbia, [e-mail: zdolicanin@np.ac.rs](e-mail: zdolicanin@np.ac.rs)  \n4 University of Kragujevac, Faculty of Medical Sciences, Svetozara Markovića 69, 34000 Kragujevac, Serbia, [e-mail: ](e-mail: ginbabic@medf.kg.ac.rs)[ginbabic@medf.kg.ac.rs](e-mail: ginbabic@medf.kg.ac.rs)  \n* Corresponding author  \nDOI: 10.46793/ICCBI23 . 140A  \nAbstract: Thrombophilia in pregnancy is the result of a complex interaction of inherited and acquired factors, which increase blood coagulation and consequently placental ischemic conditions. Early identification of risk of developing thrombophilia in pregnancy is crucial for implementing preventive measures and personalized therapy. In this study, we propose a novel approach for prediction of thrombophilia in pregnancy utilizing machine learning (ML) algorithms with a particular focus on neural networks. The research is done using a dataset consisting of demographic, lifestyle, and clinical information from a 35 pregnant woman (22 healthy and 13 with thrombophilia) . These features are used to train and evaluate different ML models with neural networks and decision trees. The evaluation of the proposed approach involves cross-validation and performance metrics assessment. The results highlight the effectiveness of decision trees and neural networks in accurately predicting thrombophilia in pregnancy risk.  \nKeywords: neural networks, decision trees, machine learning, thrombophilia in pregnancy, prediction  \n1. Introduction  \nThrombophilia is inherited or acquired disorder of hemostasis and represents condition of increased hypercoagulability that predisposes patients to thromboembolic events. In normal pregnancy, hormonal changes induce hypercoagulability state, which is adaptive mechanism to reduce the risk of hemorrhage during and after the delivery. The risk of thromboembolic events in pregnancy and postpartum period is 5 times greater compared to non-pregnant women. Thrombophilia in pregnancy is the result of a  \ncomplex interaction of inherited and acquired factors, which increase blood coagulation. It is characterized by the generation of microthrombi, which causes reduction in uteroplacental blood flow and consequently placental ischemic conditions. Mutations FV Leiden (G1691A), FII (G20210A), MTHFR (C677T) and PAI-1 are the most common genetic risk factors for thromboembolism. Several environmental factors and stress are associated with the occurrence of thrombophilia in pregnancy. However, many environmental factors are not considered in the assessment of the risk of developing thrombophilia in pregnancy due to their still unknown influence on the etiology of this disease. Identification of environmental risk factors associated with genetic predisposition to thrombophilia in pregnancy would enable individual assessment of the risk of developing this disease, and therefore more adequate prevention and therapy [1] .  \nMachine learning (ML) algorithms have emerged as powerful tools for analyzing complex clinical data and making accurate predictions. By leveraging ML techniques, researchers have made significant progress in various medical domains, including disease diagnosis,","cbCailQsjAlbTMuV","https://ap.wps.com/l/cbCailQsjAlbTMuV","pdf",347997,1,4,"English","en",105,"# Abstract\n# Introduction\n## Thrombophilia in pregnancy and clinical rationale\n## Role of machine learning in clinical prediction\n# Materials and Methods\n## Study population and diagnosis criteria\n## Data collection and transformation for ML","[{\"question\":\"Why is early identification of thrombophilia risk during pregnancy important?\",\"answer\":\"Early identification enables preventive measures and personalized therapy. It supports timely healthcare decisions for high-risk pregnant women.\"},{\"question\":\"What data were used to train the machine learning models?\",\"answer\":\"The study used demographic, lifestyle, and clinical information, along with affective-style questionnaire data, for each participant.\"},{\"question\":\"Which machine learning methods showed effective prediction results?\",\"answer\":\"Decision trees and neural networks were highlighted as effective. The evaluation used cross-validation and performance metrics to assess accuracy.\"}]","Thrombophilia Prediction Using Machine Learning Algorithms | PDF",1785728647,10,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":28},"thrombophilia-prediction-using-machine-learning-algorithms","",{"@graph":36,"@context":84},[37,53,67],{"@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":21},"https://docshare.wps.com/document/thrombophilia-prediction-using-machine-learning-algorithms/120191/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is early identification of thrombophilia risk during pregnancy important?","Question",{"text":74,"@type":75},"Early identification enables preventive measures and personalized therapy. It supports timely healthcare decisions for high-risk pregnant women.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What data were used to train the machine learning models?",{"text":79,"@type":75},"The study used demographic, lifestyle, and clinical information, along with affective-style questionnaire data, for each participant.",{"name":81,"@type":72,"acceptedAnswer":82},"Which machine learning methods showed effective prediction results?",{"text":83,"@type":75},"Decision trees and neural networks were highlighted as effective. 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