Currently, the risk factors of pregnancy loss are increasing andare considered a major challenge because they vary between cases. The earlyprediction of miscarriage can help pregnant ladies to take the needed careand ...Currently, the risk factors of pregnancy loss are increasing andare considered a major challenge because they vary between cases. The earlyprediction of miscarriage can help pregnant ladies to take the needed careand avoid any danger. Therefore, an intelligent automated solution must bedeveloped to predict the risk factors for pregnancy loss at an early stage toassist with accurate and effective diagnosis. Machine learning (ML)-baseddecision support systems are increasingly used in the healthcare sector andhave achieved notable performance and objectiveness in disease predictionand prognosis. Thus, we developed a model to help obstetricians predictthe probability of miscarriage using ML. And support their decisions andexpectations about pregnancy status by providing an easy, automated way topredict miscarriage at early stages using ML tools and techniques. Althoughmany published papers proposed similar models, none of them used Saudiclinical data. Our proposed solution used ML classification algorithms tobuild a miscarriage prediction model. Four classifiers were used in this study:decision tree (DT), random forest (RF), k-nearest neighbor (KNN), andgradient boosting (GB). Accuracy, Precision, Recall, F1-score, and receiveroperating characteristic area under the curve (ROC-AUC) were used to evaluatethe proposed model. The results showed that GB overperformed the otherclassifiers with an accuracy of 93.4% and ROC-AUC of 97%. This proposedmodel can assist in the early identification of at-risk pregnant women to avoidmiscarriage in the first trimester and will improve the healthcare sector inSaudi Arabia.展开更多
文摘Currently, the risk factors of pregnancy loss are increasing andare considered a major challenge because they vary between cases. The earlyprediction of miscarriage can help pregnant ladies to take the needed careand avoid any danger. Therefore, an intelligent automated solution must bedeveloped to predict the risk factors for pregnancy loss at an early stage toassist with accurate and effective diagnosis. Machine learning (ML)-baseddecision support systems are increasingly used in the healthcare sector andhave achieved notable performance and objectiveness in disease predictionand prognosis. Thus, we developed a model to help obstetricians predictthe probability of miscarriage using ML. And support their decisions andexpectations about pregnancy status by providing an easy, automated way topredict miscarriage at early stages using ML tools and techniques. Althoughmany published papers proposed similar models, none of them used Saudiclinical data. Our proposed solution used ML classification algorithms tobuild a miscarriage prediction model. Four classifiers were used in this study:decision tree (DT), random forest (RF), k-nearest neighbor (KNN), andgradient boosting (GB). Accuracy, Precision, Recall, F1-score, and receiveroperating characteristic area under the curve (ROC-AUC) were used to evaluatethe proposed model. The results showed that GB overperformed the otherclassifiers with an accuracy of 93.4% and ROC-AUC of 97%. This proposedmodel can assist in the early identification of at-risk pregnant women to avoidmiscarriage in the first trimester and will improve the healthcare sector inSaudi Arabia.
