Abstract
Preeclampsia is a condition that only occurs during a woman's pregnancy and is identified by a rise in the expecting patient's blood pressure, often after the 20th week of pregnancy. It is one of the top three causes of mortality among pregnant women worldwide. Accurate preeclampsia risk prediction would allow more effective, risk-based maternal care pathways. Delivering accurate preeclampsia risk assessment ranging from high to low requires feasible biomarkers. The maternal health risk public dataset provided by Oslo University Hospital, Oslo, Norway was used in this work. The data was collected from different hospitals, community clinics, and maternal health cares at Oslo University Hospital, (Oslo, Norway) through the IoT-based risk monitoring system. The dataset includes biomarkers/indicators such as heart rate, blood glucose levels, diastolic and systolic blood pressure, body temperature, and others. These five most important biomarkers should be kept under their respective normal levels as they play a vital role in predicting risks during pregnancy. The machine learning techniques for predicting various risk levels, including Naïve Bayes (NB), logistic regression (LR), Ada boost (AB), support vector models (SVM), decision tree models, the k-nearest-neighbor algorithm (KNN), and random forest (RF) are used in this work. These supervised machine learning tools gave an accurate prediction of the preeclampsia risk level, with the experimental results giving the highest accuracy to random forest (RF) of 96.39%, among the used machine learning tools.
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Swathikrishna, M.R., Sriram, S., Subha, B. (2024). Preeclampsia Risk Prediction Using Machine Learning Algorithms. In: Joshi, A., Mahmud, M., Ragel, R.G., Karthik, S. (eds) ICT: Innovation and Computing. ICTCS 2023. Lecture Notes in Networks and Systems, vol 879. Springer, Singapore. https://doi.org/10.1007/978-981-99-9486-1_5
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DOI: https://doi.org/10.1007/978-981-99-9486-1_5
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