[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127219-en":3,"doc-seo-127219-105":30,"detail-sidebar-cat-0-en-105":96},{"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},127219,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Machine Learning for Preeclampsia Prediction - Enhancing Screening in Primary Health Care","Preeclampsia is a major cause of maternal morbidity and mortality worldwide, making early detection essential to reduce adverse outcomes. This study developed a machine learning prediction model using routinely available maternal characteristics, including body mass index, mean arterial pressure, and a clinical history of hypertension or diabetes mellitus. Secondary data from 2,250 pregnancies were processed to address missing values and class imbalance. Support vector machines with threshold adjustment performed best.","Kesmas  \n\n| Volume 20\u003Cbr>Issue 2 May 2025 | Article 8 |\n| --- | --- |\n| 5-30-2025\u003Cbr>Machine Learning for Preeclampsia Prediction: Enhancing Screening in Primary Health Care\u003Cbr>Dwirani Amelia\u003Cbr>University of Indonesia, Jakarta, Indonesia, [dwiraniamelia@gmail.com](dwiraniamelia@gmail.com)\u003Cbr>Asri Adisasmita\u003Cbr>Universitas Indonesia, Depok, [aadisasmita@gmail.com](aadisasmita@gmail.com)\u003Cbr>[Kemal N. Siregar](Kemal N. Siregar)\u003Cbr>Universitas Indonesia, Depok, [nazarudin.kemal51@gmail.com](nazarudin.kemal51@gmail.com)\u003Cbr>Detty Siti Nurdiati\u003Cbr>Universitas Gadjah Mada, Yogyakarta, [detty@ugm.ac.id](detty@ugm.ac.id)\u003Cbr>Follow this and additional works at: [https://scholarhub.ui.ac.id/kesmas](https://scholarhub.ui.ac.id/kesmas)\u003Cbr> Part of the Clinical Epidemiology Commons, Epidemiology Commons, and the Maternal and Child Health Commons |  |\n\nRecommended Citation  \nAmelia D , Adisasmita A , Siregar KN , et al. Machine Learning for Preeclampsia Prediction: Enhancing Screening in Primary Health Care. Kesmas. 2025; 20(2): 147-156  \nDOI: 10.7454/kesmas.v20i2 .2243  \nAvailable at: [https://scholarhub.ui.ac.id/kesmas/vol20/iss2/8](https://scholarhub.ui.ac.id/kesmas/vol20/iss2/8)  \nThis Original Article is brought to you for free and open access by the Faculty of Public Health at UI Scholars Hub. It has been accepted for inclusion in Kesmas by an authorized editor of UI Scholars Hub.  \nMachine Learning for Preeclampsia Prediction: Enhancing Screening in Primary Health Care  \nDwirani Amelia1,2*, Asri Adisasmita3, Kemal N Siregar4, Detty Siti Nurdiati5  \n1Doctoral Program, Department of Epidemiology, Faculty of Public Health, Universitas Indonesia, Depok, Indonesia  \n2Budi Kemuliaan Health Institute, Jakarta, Indonesia  \n3Department of Epidemiology, Faculty of Public Health, Universitas Indonesia, Depok, Indonesia  \n4Department of Biostatistics and Population Studies, Faculty of Public Health, Universitas Indonesia, Depok, Indonesia  \n5Department of Obstetrics and Gynecology, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Dr. Sardjito Hospital, Yogyakarta, Indonesia  \nAbstract  \nPreeclampsia is a leading cause of maternal morbidity and mortality worldwide, with early detection being critical for reducing adverse outcomes. This study aimed to develop a machine learning model for predicting the risk of preeclampsia using readily available maternal characteristics such as body mass index, mean arterial pressure, and clinical history of hypertension or diabetes mellitus. Secondary data from 2,250 pregnancies were analyzed, addressing challenges such as missing data and class imbalance through preprocessing. Various algorithms, including support vector machines, random forest, and logistic regression, were evaluated. Herein, a support vector machines model with threshold adjustment showed the best performance, with a sensitivity of 67.5%, specificity of 57.23%, and an area under the curve of 0.68. These findings indicated the promising potential of scalable and interpretable prediction models for enhancing preeclampsia screening in primary health care settings. However, further refinement and validation of the proposed model are required for broader clinical integration to improve maternal and neonatal health outcomes.  \nKeywords: machine learning, maternal health, preeclampsia prediction, primary care screening  \nIntroduction  \nPreeclampsia (PE) is a multifactorial syndrome and a leading cause of maternal morbidity and mortality globally.1 Pregnancy-induced hypertension causes one-fifth of maternal deaths worldwide, and PE/eclampsia alone is estimated to cause 60,000 to 80,000 maternal deaths annually.2,3 PE is characterized by elevated blood pressure and multisystem manifestations, and its etiology is poorly understood. The prevalence and incidence of PE vary globally, with the global incidence rate of severe PE estimated between 2% and 10% of all pregnancies.4-6 Later evidence showed an increase in the incidence of h","cbCaim76MBgdvYfL","https://ap.wps.com/l/cbCaim76MBgdvYfL","pdf",607095,1,11,"English","en",105,"# Introduction\n## Rationale for early detection and prediction approaches\n# Methods\n## Data source and preprocessing\n## Model development and evaluation\n# Results\n## Predictive performance and trade-offs\n# Discussion\n## Clinical implications and need for further validation","[{\"question\":\"What goal does the study address in preeclampsia care?\",\"answer\":\"The study aims to build a machine learning model to predict preeclampsia risk early, improving screening effectiveness in primary health care.\"},{\"question\":\"Which maternal inputs are used for prediction?\",\"answer\":\"The model uses routinely available maternal characteristics such as body mass index, mean arterial pressure, and clinical history of hypertension or diabetes mellitus.\"},{\"question\":\"How was data quality and modeling difficulty handled?\",\"answer\":\"Secondary data from 2,250 pregnancies were analyzed with preprocessing steps to manage missing data and class imbalance.\"},{\"question\":\"Which algorithm showed the best performance, and what did it achieve?\",\"answer\":\"A support vector machines model with threshold adjustment achieved the best performance, reporting sensitivity of 67.5%, specificity of 57.23%, and AUC of 0.68.\"}]","Machine Learning for Preeclampsia Prediction - 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