The increasing use of interconnected sensors to monitor patients with chronic diseases, integrated with tools for the management of shared information, can guarantee a better performance of health information systems (HISs) by performing personalized healthcare. The early diagnosis of chronic diseases such as hypertensive disorders of pregnancy represents a significant challenge in women's healthcare. Computational learning techniques are useful tools for pattern recognition in the assessment of an increasing amount of integrated data related to these diseases. Hence, in this paper, the use of machine learning (ML) techniques is proposed for the assessment of real data referred to hypertensive disorders in pregnancy. The results show that the averaged one-dependence estimator algorithm can help in the decision- making process in uncertain moments, thus improving the early detection of these chronic diseases. The best-evaluated computational learning algorithm improves the performance of HISs through its precise diagnosis. This method can be applied in electronic health (e-health) environments as a useful tool for handling uncertainty in the decision-making process related to high-risk pregnancy.

中文翻译：

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个性化妊娠护理的计算学习方法


越来越多地使用互连传感器来监测慢性病患者，并与共享信息管理工具集成，可以通过执行个性化医疗保健来保证健康信息系统（HIS）的更好性能。妊娠高血压疾病等慢性疾病的早期诊断对女性医疗保健提出了重大挑战。计算学习技术是模式识别的有用工具，可用于评估与这些疾病相关的越来越多的综合数据。因此，在本文中，建议使用机器学习（ML）技术来评估妊娠期高血压疾病的真实数据。结果表明，平均一相关估计算法可以帮助不确定时刻的决策过程，从而提高这些慢性疾病的早期发现。评价最佳的计算学习算法通过精确的诊断提高了 HIS 的性能。该方法可以应用于电子健康（e-health）环境中，作为处理与高危妊娠相关的决策过程中的不确定性的有用工具。