Al Mashrafi Sulaiman Salim, Tafakori Laleh, Abdollahian Mali
School of Science, RMIT University, Melbourne, Victoria, Australia.
Department of Information and Statistics, Directorate General of planning, Ministry of Health, Muscat, Oman.
BMC Pregnancy Childbirth. 2024 Dec 18;24(1):820. doi: 10.1186/s12884-024-07030-9.
Maternal morbidity and mortality remain critical health concerns globally. As a result, reducing the maternal mortality ratio (MMR) is part of goal 3 in the global sustainable development goals (SDGs), and previously, it was an important indicator in the Millennium Development Goals (MDGs). Therefore, identifying high-risk groups during pregnancy is crucial for decision-makers and medical practitioners to mitigate mortality and morbidity. However, the availability of accurate predictive models for maternal mortality and maternal health risks is challenging. Compared with traditional predictive models, machine learning algorithms have emerged as promising predictive modelling methods providing accurate predictive models.
This work aims to explore the potential of machine learning (ML) algorithms in maternal risk level prediction using a nationwide maternal mortality dataset from Oman for the first time. A total of 402 maternal deaths from 1991 to 2023 in Oman were included in this study. We utilised principal component analysis (PCA) in the ML algorithms and compared them to the results of model performance without PCA. We employed and compared ten ML algorithms, including decision tree (DT), random forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Extreme Gradient Boosting (xgboost), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Logistic Regression (LR), Support Vector Machine (SVM) and Artificial Neural Network (ANN). Different metrics, including, accuracy, sensitivity, precision, and the F1- score, were utilised to assess Model performance.
The results indicated that the RF model outperformed the other methods in predicting the risk level (low or high) with an accuracy of 75.2%, precision of 85.7% and F1- score of 73% after PCA was applied.
We applied several machine learning models to predict maternal risk levels for the first time using real data from Oman. RF outperformed the other algorithms in this classification problem. A reliable estimate of maternal risk level would facilitate intervention plans for medical practitioners to reduce maternal death.
孕产妇发病率和死亡率仍是全球重要的健康问题。因此，降低孕产妇死亡率是全球可持续发展目标（SDGs）中目标3的一部分，并且在此之前，它也是千年发展目标（MDGs）中的一项重要指标。所以，识别孕期高危人群对于决策者和医疗从业者降低死亡率和发病率至关重要。然而，获得准确的孕产妇死亡率和孕产妇健康风险预测模型具有挑战性。与传统预测模型相比，机器学习算法已成为有前景的预测建模方法，能够提供准确的预测模型。
本研究首次使用阿曼全国范围的孕产妇死亡数据集，旨在探索机器学习（ML）算法在孕产妇风险水平预测中的潜力。本研究纳入了阿曼1991年至2023年期间共402例孕产妇死亡病例。我们在机器学习算法中使用了主成分分析（PCA），并将其结果与未使用PCA的模型性能结果进行比较。我们采用并比较了十种机器学习算法，包括决策树（DT）、随机森林（RF）、K近邻（KNN）、朴素贝叶斯（NB）、极端梯度提升（xgboost）、线性判别分析（LDA）、二次判别分析（QDA）、逻辑回归（LR）、支持向量机（SVM）和人工神经网络（ANN）。使用了不同的指标，包括准确率、灵敏度、精确率和F1分数，来评估模型性能。
结果表明，在应用PCA后，随机森林（RF）模型在预测风险水平（低或高）方面优于其他方法，准确率为75.2%，精确率为85.7%，F1分数为73%。
我们首次使用阿曼的真实数据应用了几种机器学习模型来预测孕产妇风险水平。在这个分类问题中，随机森林（RF）优于其他算法。对孕产妇风险水平的可靠估计将有助于医疗从业者制定干预计划以降低孕产妇死亡。
