Biomedical data analytics in mobile-health environments for high-risk pregnancy outcome prediction
| Autor: | Francisco Herlânio Costa Carvalho, Mario W. L. Moreira, Joel J. P. C. Rodrigues, Naveen Chilamkurti, Jalal Al-Muhtadi, Victor M. Denisov |
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| Rok vydání: | 2019 |
| Předmět: |
020205 medical informatics
General Computer Science Computer science Developing country Computational intelligence Maternal morbidity 02 engineering and technology Machine learning computer.software_genre Bayes' theorem 0202 electrical engineering electronic engineering information engineering medicine Pregnancy Fetal death business.industry Bayesian network medicine.disease Analytics Gestation 020201 artificial intelligence & image processing Artificial intelligence business Mobile device computer Developed country High risk pregnancy |
| Zdroj: | Journal of Ambient Intelligence and Humanized Computing. 10:4121-4134 |
| ISSN: | 1868-5145 1868-5137 |
| DOI: | 10.1007/s12652-019-01230-4 |
| Popis: | According to the World Health Organization (WHO), a significant reduction in mortality and maternal morbidity has occurred in developed countries over the past decades. In contrast, these rates remain high in developing countries. Smart mobile-health (m-health) applications that use machine learning (ML) approaches are necessary tools for pregnancy monitoring in an accessible, reliable, and cost-efficient manner, making the prediction of high-risk situations possible during gestation. This paper, therefore, proposes the development, performance evaluation, and comparison of ML algorithms based on Bayesian networks capable of identifying at-risk pregnancies based on the symptoms and risk factors presented by the patients. A performance comparison of several Bayes-based ML algorithms determined the best-suited algorithm for the prediction, identification, and accompaniment of hypertensive disorders during pregnancy. The contribution of this study focuses on finding a smart classifier for the development of novel mobile devices, which presents reliable results in the identification of problems related to pregnancy. Through the well-known cross-validation method, this proposal is evaluated and compared with other recent approaches. The averaged one-dependence estimators presented better results on average than the other approaches. These findings are key to improving the health monitoring of women suffering from high-risk pregnancies around the world. Thus, this study can contribute to a reduction of both maternal and fetal deaths. |
| Databáze: | OpenAIRE |
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