Prediction of Current Maternal Health Risk Levels Get Pregnant Using the Decision Tree Algorithm
Keywords:
Decision Tree, Machine Learning, Maternal Health, Risk Classification, Risk PredictionAbstract
This study aimed to predict current maternal health risk levels during pregnancy using a Decision Tree algorithm and to evaluate its ability to distinguish high-, mid-, and low-risk cases based on routinely available clinical attributes. A quantitative computational classification design was applied to 1,025 maternal health records obtained from the UCI Repository. Six numerical predictors were analyzed, comprising age, systolic blood pressure, diastolic blood pressure, blood sugar, body temperature, and average heart rate, with maternal health risk level serving as the multiclass target variable. Data preparation included validation, integration and transformation, and reduction or discretization before modeling in RapidMiner. Model performance was evaluated using a confusion matrix, overall accuracy, precision, and recall. The Decision Tree achieved an overall accuracy of 71.92%. High-risk cases obtained 88.53% recall and 84.28% precision, while low-risk cases reached 92.31% recall and 65.79% precision. Mid-risk cases showed weaker performance, with 33.83% recall and 71.65% precision, indicating substantial misclassification into neighboring categories, particularly the low-risk class. The findings show that the Decision Tree provides interpretable multiclass maternal-risk prediction and performs more reliably for high- and low-risk cases than for mid-risk cases. The model may support transparent maternal-health decision-support development, although further refinement and validation are required to improve intermediate-risk classification.
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