基于贝叶斯分类器的孕期护理推理机制

Mário W. L. Moreira, J. Rodrigues, Antonio M. B. Oliveira, K. Saleem, Augusto J. V. Neto
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引用次数: 21

摘要

智能决策支持系统(DSSs)的发展取得了重大进展,影响了妊娠护理的重要结果。然而,即使考虑到为减少与怀孕有关的问题造成的妇女死亡人数所作的努力,这种减少的影响也不如人类发展的其他领域。妊娠期高血压疾病,特别是先兆子痫和子痫,在围产期发病率和孕产妇死亡率中占很大比例。在此背景下,本文提出了一个推理模型,该模型使用能够在数据集中操作的数据挖掘(DM)技术来提取模式并协助知识发现。识别使妊娠复杂化的高血压危象,可以显著降低孕妇的后遗症和死亡发生率。在这项工作中,两种贝叶斯分类器进行比较,以更好地分类高血压疾病的严重程度。结果表明,Naïve贝叶斯分类器性能优异,与其他实验分类器相比,具有更好的精度和F-measure。即使发现了预测高血压疾病的良好表现,也需要评估其他贝叶斯方法,以及其他DM技术,如基于人工智能(AI)和基于树的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An inference mechanism using Bayes-based classifiers in pregnancy care
Significant advances on smart decision support systems (DSSs) development have influenced important results on pregnancy care. Nevertheless, even considering the efforts to reduce the number of women deaths due to problems related to pregnancy, this decrease presented less impact than other areas of human development. Hypertensive disorders in pregnancy, particularly pre-eclampsia and eclampsia, account for significant proportion of perinatal morbidity and maternal mortality. In this context, this paper proposes an inference model that uses data mining (DM) techniques capable for operating in a data set to extract patterns and assist in knowledge discovery. Identifying hypertensive crises that complicate pregnancy, it can impact in a meaningful reduction the incidence of sequelae and death of pregnant women. Comparison between two Bayesian classifiers is performed in this work to better classify the hypertensive disorders severity. Results showed that Naïve Bayes classifier had an excellent performance, presenting better precision and F-measure, compared to the other experimented classifiers. Even finding a good performance to predict hypertensive disorders, other Bayesian methods need to be evaluated, as well as other DM techniques such as those based on artificial intelligence (AI) and tree-based methods.
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