<![CDATA[Preeclampsia is a pregnancy complication that significantly contributes to increased maternal and perinatal morbidity and mortality. Early detection remains challenging, as most prediction systems rely solely on structured clinical data and have yet to integrate unstructured information in the form of patient complaints. This study aims to develop a preeclampsia prediction model using the XGBoost algorithm by combining clinical data and text representations of patient complaints processed through Natural Language Processing (NLP). The main contribution of this study is an empirical evaluation of the effectiveness of incorporating NLP-based features in improving model performance compared to a model using clinical variables alone. The dataset consists of 100 medical records of pregnant women classified into Preeclampsia and Non-Preeclampsia classes. Complaint texts were represented using the Term Frequency–Inverse Document Frequency (TF‑IDF) method. Testing was conducted in three scenarios: Clinical Model, NLP Model, and Clinical+NLP Model. The results showed that the Clinical Model achieved an accuracy of 85% and an AUC-ROC of 0.92; the NLP Model yielded an accuracy of 75% and an AUC-ROC of 0.58; while the Clinical+NLP Model obtained an accuracy of 85% and an AUC-ROC of 0.91. However, evaluation metrics highly relevant for early detection Recall (sensitivity) and F1-score were not reported in this study. Both metrics are critically important given the high risk of undetected preeclampsia cases and should therefore be a primary focus in future development. The results clearly demonstrate that the addition of NLP features did not improve the performance of the clinical model; accuracy remained identical and AUC-ROC in fact decreased slightly. These findings confirm that the developed model is more appropriately utilized as a supportive early screening tool and is not intended to replace clinical diagnosis.]]>
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