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Presenting a prediction model for HELLP syndrome through data mining
Boshra Farajollahi1, Mohammadjavad Sayadi2, Mostafa Langarizadeh1
1Department of Health Information Management, School of Health Management and Information Sciences, University of Medical Sciences, Tehran, Iran.
Insights
Machine learning models accurately diagnose HELLP syndrome using non-invasive parameters. Biomarker features significantly improve diagnostic accuracy for this complex pregnancy complication.
Area of Science:
- Obstetrics and Gynecology
- Medical Informatics
- Machine Learning in Healthcare
Background:
- HELLP syndrome involves hemolysis, elevated liver enzymes, and low platelets.
- Its complex pathogenesis and overlap with other conditions complicate diagnosis.
- Delayed diagnosis of HELLP syndrome hinders effective management.
Purpose of the Study:
- To develop and evaluate machine learning models for diagnosing HELLP syndrome.
- To utilize non-invasive parameters for improved diagnostic capabilities.
- To identify key predictive features for HELLP syndrome.
Main Methods:
- A cross-sectional study involving 384 patients over 11 years.
- Data preprocessing and machine learning model implementation.
- Evaluation of various algorithms including deep learning, KNN, RF, and LR.
Main Results:
- Multi-layer perceptron and deep learning achieved over 99% F1 score.
- Several algorithms (KNN, RF, AdaBoost, XGBoost, LR) exceeded 0.95 F1 score.
- Platelet count, gestational age, and ALT were identified as crucial diagnostic variables.
Conclusions:
- Machine learning algorithms demonstrate high efficacy in HELLP syndrome diagnosis.
- Biomarker features significantly contribute to the diagnostic accuracy of HELLP syndrome.
- Most tested ML models, excluding decision trees, achieved F1 scores above 0.90.
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.
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