A preeclampsia diagnosis approach using Bayesian networks

Moreira, Mário W. L.; Rodrigues, Joel J. P. C.; Oliveira, Antônio M. B.; Ramos, Ronaldo; Saleem, Kashif

doi:10.1109/icc.2016.7510893

Cited by 22 publications

(6 citation statements)

References 14 publications

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“…Cloud-based data mining and IoT services [70][71][72], data-driven ML models (e.g., biomarkers, neural network models, Bayesian networks and classifiers, rule-based tree models, random forest approaches, particle swarm optimization, boosting models) [73][74][75][76][77][78][79][80], and mobile healthcare (mHealth) applications are used to monitor maternal health data, provide adequate maternal healthcare, and predict pregnancy risks (e.g., hypertension, preeclampsia, gestational diabetes). mHealth services are applied to detect early risk of maternal mortality using social, demographic, gynecological, and obstetric predictor variables [81], to identify maternity risks based on a pregnancy database to support decision-making [82], and to improve maternal care through mobile diagnostics by accessing data that predict early symptoms of hypertension and recommending countermeasures [83].…”

Section: Sdg 3: Good Health and Wellbeingmentioning

confidence: 99%

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

et al. 2022

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The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.

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Section: Sdg 3: Good Health and Wellbeingmentioning

confidence: 99%

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

et al. 2022

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“…BN models have widely been used for diagnostic analysis in different domains including agriculture [11], cyber security [12][13][14][15], health care [16][17][18][19][20][21][22], and transportation [23][24][25]. Chen et al [11] proposed a two-layer BN for maize disease diagnosis.…”

Section: Diagnostic Bayesian Networkmentioning

confidence: 99%

Combining Bayesian Networks and Fishbone Diagrams to Distinguish Between Intentional Attacks and Accidental Technical Failures

Chockalingam

Pieters

Teixeira

et al. 2019

Graphical Models for Security

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Because of modern societies' dependence on industrial control systems, adequate response to system failures is essential. In order to take appropriate measures, it is crucial for operators to be able to distinguish between intentional attacks and accidental technical failures. However, adequate decision support for this matter is lacking. In this paper, we use Bayesian Networks (BNs) to distinguish between intentional attacks and accidental technical failures, based on contributory factors and observations (or test results). To facilitate knowledge elicitation, we use extended fishbone diagrams for discussions with experts, and then translate those into the BN formalism. We demonstrate the methodology using an example in a case study from the water management domain.

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“…Mario W.L. Moreira et al [5] [6] [10] has made research on predicted preeclampsia using data mining. The modeling is the Bayesian network.…”

Section: Related Workmentioning

confidence: 99%

Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization

Tahir¹,

Badriyah²,

Syarif³

2018

emitter

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Preeclampsia is a pregnancy abnormality that develops after 20 weeks of pregnancy characterized by hypertension and proteinuria. Â The purpose of this research was to predict the risk of preeclampsia level in pregnant women during pregnancy process using Neural Network and Deep Learning algorithm, and compare the result of both algorithm. There are 17 parameters that taken from 1077 patient data in Haji General Hospital Surabaya and two hospitals in Makassar start on December 12th 2017 until February 12th 2018. We use particle swarm optimization (PSO) as the feature selection algorithm. This experiment shows that PSO can reduce the number of attributes from 17 to 7 attributes. Using LOO validation on the original data show that the result of Deep Learning has the accuracy of 95.12% and it give faster execution time by using the reduced dataset (eight-speed quicker than the original data performance). Beside that the accuracy of Deep Learning increased 0.56% become 95.68%. Generally, PSO gave the excellent result in the significantly lowering sum attribute as long as keep and improve method and precision although lowering computational period. Deep Learning enables end-to-end framework, and only need input and output without require for tweaking the attributes or features and does not require a long time and complex systems and understanding of the deep data on computing.

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A preeclampsia diagnosis approach using Bayesian networks

Cited by 22 publications

References 14 publications

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

Combining Bayesian Networks and Fishbone Diagrams to Distinguish Between Intentional Attacks and Accidental Technical Failures

Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization

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