ACME:基于贝叶斯网络分类器和非冗余特征选择方法的子痫前期风险分类模型

Franklin Parrales-Bravo, Rosangela Caicedo-Quiroz, Elianne Rodríguez-Larraburu, Julio Barzola-Monteses
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引用次数: 0

摘要

虽然子痫前期是瓜亚斯省(厄瓜多尔)产妇死亡的主要原因,但对其病因尚未进行深入研究。本研究的目的是建立一个贝叶斯网络分类器,用于诊断子痫前期病例,同时促进对该疾病病因的了解。研究人员从厄瓜多尔瓜亚基尔市 "IESS Los Ceibos "医院接受治疗的患者病历中回顾性收集了 2017 年至 2023 年的数据。在建立可解释的分类模型时,考虑了奈夫贝叶斯(NB)、周柳树增强奈夫贝叶斯(TANcl)和半奈夫贝叶斯(FSSJ)算法。为完成特征选择任务,提出了一种非冗余特征选择方法(NoReFS)。使用 TANcl 和 NoReFS 训练的模型是其中最好的,准确率接近 90%。根据最佳模型,年龄在 35 岁以上、患有严重阴道感染、生活在农村地区、吸烟、有糖尿病家族史和高血压病史的患者是子痫前期的高危人群。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ACME: A Classification Model for Explaining the Risk of Preeclampsia Based on Bayesian Network Classifiers and a Non-Redundant Feature Selection Approach
While preeclampsia is the leading cause of maternal death in Guayas province (Ecuador), its causes have not yet been studied in depth. The objective of this research is to build a Bayesian network classifier to diagnose cases of preeclampsia while facilitating the understanding of the causes that generate this disease. Data for the years 2017 through 2023 were gathered retrospectively from medical histories of patients treated at “IESS Los Ceibos” hospital in Guayaquil, Ecuador. Naïve Bayes (NB), The Chow–Liu Tree-Augmented Naïve Bayes (TANcl), and Semi Naïve Bayes (FSSJ) algorithms have been considered for building explainable classification models. A proposed Non-Redundant Feature Selection approach (NoReFS) is proposed to perform the feature selection task. The model trained with the TANcl and NoReFS was the best of them, with an accuracy close to 90%. According to the best model, patients whose age is above 35 years, have a severe vaginal infection, live in a rural area, use tobacco, have a family history of diabetes, and have had a personal history of hypertension are those with a high risk of developing preeclampsia.
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