Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

Abstract

Background. Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression. Objective. The purpose of this study was to use machine learning methods to predict GDM and compare their performance with that of logistic regressions. Methods. We performed a retrospective, observational study including women who attended their routine first hospital visits during early pregnancy and had Down’s syndrome screening at 16-20 gestational weeks in a tertiary maternity hospital in China from 2013.1.1 to 2017.12.31. A total of 22,242 singleton pregnancies were included, and 3182 (14.31%) women developed GDM. Candidate predictors included maternal demographic characteristics and medical history (maternal factors) and laboratory values at early pregnancy. The models were derived from the first 70% of the data and then validated with the next 30%. Variables were trained in different machine learning models and traditional logistic regression models. Eight common machine learning methods (GDBT, AdaBoost, LGB, Logistic, Vote, XGB, Decision Tree, and Random Forest) and two common regressions (stepwise logistic regression and logistic regression with RCS) were implemented to predict the occurrence of GDM. Models were compared on discrimination and calibration metrics. Results. In the validation dataset, the machine learning and logistic regression models performed moderately (AUC 0.59-0.74). Overall, the GBDT model performed best (AUC 0.74, 95% CI 0.71-0.76) among the machine learning methods, with negligible differences between them. Fasting blood glucose, HbA1c, triglycerides, and BMI strongly contributed to GDM. A cutoff point for the predictive value at 0.3 in the GBDT model had a negative predictive value of 74.1% (95% CI 69.5%-78.2%) and a sensitivity of 90% (95% CI 88.0%-91.7%), and the cutoff point at 0.7 had a positive predictive value of 93.2% (95% CI 88.2%-96.1%) and a specificity of 99% (95% CI 98.2%-99.4%). Conclusion. In this study, we found that several machine learning methods did not outperform logistic regression in predicting GDM. We developed a model with cutoff points for risk stratification of GDM.

Data-Driven Modeling of Pregnancy-Related Complications

“…Machine learning is well suited for predictive modeling of pregnancy outcomes [91,92] and is becoming more prevalent [93][94][95][96][97][98][99][100][101][102][103][104][105] due to its ability to model highly complex relationships between measured features and outcomes. The majority of previous work focused on modeling techniques that incorporate one or two data sources, including clinical [95] as well as derived numerical data from another source such as blood samples [39], Doppler ultrasound, echosonography, or magnetic resonance imaging (MRI) readings [101], or mental health assessments [106].…”

Has the Flood Entered the Basement? A Systematic Literature Review about Machine Learning in Laboratory Medicine

“…Ye et al [ 51 ] compared the performance of many ML models and logistic regression for predicting gestational diabetes (GDM) using routine lab tests. They chose 104 variables (medical history, clinical assessment, ultrasonic screening data, biochemical data and data from Down’s screening), and they included 22,242 women in the study.…”

“…However, clarity and simplicity are the reasons why logistic regression was also frequently chosen [ 36 , 56 ], both as a stand-alone model and also as a baseline model to be compared with other, more complex (and hence less generalisable) models. Among the best models, those in the Ensemble family (e.g., XGB, GBT) were chosen both for their medium–high performance [ 21 , 30 , 32 , 33 , 44 , 51 , 58 , 61 ] and their training speed. Models in the DL family [ 27 , 35 , 37 , 49 , 52 , 53 , 60 ], especially RNN and ANN, have been increasingly chosen in recent years.…”

“…It follows that the choice to build an ML system is not a shortcut to obtain simpler and better results in comparison to conventional methods [ 51 ]. A good ML system requires an adequate amount of data, the right quality of the data and valid management of the missing values (i.e., data imputation), a reasoned pre-selection of the variables to input into the system and the right use of the training set, validation (or tuning) set and testing set.…”

“…Over time, what kind of variables to use is becoming clearer: it has evolved from only using static data to using simple or more complex time-related representations [ 27 , 30 , 48 , 54 ], which also require novel ways to manage data incompleteness and to leverage this as an indirect source of information about the patient’s condition [ 36 ]. In this regard, some authors [ 19 , 51 ], in light of their disappointing results, have invited the medical specialist community to choose, include and study the new predictors.…”