Presenting a prediction model for HELLP syndrome through data mining

• Published in: BMC medical informatics and decision making Vol. 25; no. 1; pp. 135 - 13
• Main Authors: Farajollahi, Boshra, Sayadi, Mohammadjavad, Langarizadeh, Mostafa, Ajori, Ladan
• Format: Journal Article
• Language: English
• Published: London BioMed Central 17.03.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Accuracy; Adult; Alanine; Alanine transaminase; Algorithms; Analysis; Biomarkers; Blood pressure; Computational linguistics; Cross-Sectional Studies; Data mining; Data Mining - methods; Data processing; Decision trees; Deep learning; Delphi method; Diagnosis; Diagnosis, Noninvasive; Edema; Enzymes; Feature selection; Female; Gestational age; Health Informatics; HELLP syndrome; HELLP Syndrome - diagnosis; Humans; Hypertension; Information Systems and Communication Service; Iran; Kinases; Language processing; Literature reviews; Machine Learning; Management of Computing and Information Systems; Medical prognosis; Medical records; Medical research; Medicine; Medicine & Public Health; Medicine, Experimental; Methods; Multilayer perceptrons; Multilayers; Natural language interfaces; Pathogenesis; Patients; Performance evaluation; Platelets; Poisoning; Prediction model; Prediction models; Preeclampsia; Pregnancy; Pregnancy complications; Support vector machines; Womens health; Iran; Data mining; Prediction model; HELLP syndrome
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-025-02904-0

Background The HELLP syndrome represents three complications: hemolysis, elevated liver enzymes, and low platelet count. Since the causes and pathogenesis of HELLP syndrome are not yet fully known and well understood, distinguishing it from other pregnancy-related disorders is complicated. Furthermore, late diagnosis leads to a delay in treatment, which challenges disease management. The present study aimed to present a machine learning (ML) attitude for diagnosing HELLP syndrome based on non-invasive parameters. Method This cross-sectional study was conducted on 384 patients in Tajrish Hospital, Tehran, Iran, during 2010–2021 in four stages. In the first stage, data elements were identified using a literature review and Delphi method. Then, patient records were gathered, and in the third stage, the dataset was preprocessed and prepared for modeling. Finally, ML models were implemented, and their evaluation metrics were compared. Results A total of 21 variables were included in this study after the first stage. Among all the ML algorithms, multi-layer perceptron and deep learning performed the best, with an F1 score of more than 99%.In all three evaluation scenarios of 5fold and 10fold cross-validation, the K-nearest neighbors (KNN), random forest (RF), AdaBoost, XGBoost, and logistic regression (LR) had an F1 score of over 0.95, while this value was around 0.90 for support vector machine (SVM), and the lowest values were below 0.90 for decision tree (DT). According to the modeling output, some variables, such as platelet, gestational age, and alanine aminotransferase (ALT), were the most important in diagnosing HELLP syndrome. Conclusion The present work indicated that ML algorithms can be used successfully in the development of HELLP syndrome diagnosis models. Other algorithms besides DTs have an F1 score above 0.90. In addition, this study demonstrated that biomarker features (among all features) have the most significant impact on the diagnosis of HELLP syndrome.