Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry

Heo, Byeong Mun; Ryu, Keun Ho

doi:10.3390/ijerph15112571

Cited by 25 publications

(27 citation statements)

References 52 publications

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“…Foram encontrados 226 artigos. Após a exclusão dos duplicados e retirada dos artigos que não se encontravam entre 2016 e 2021, obteve-se um total de 22 artigos, os quais passaram por leitura de título e resumo para convergirem em 13 artigos lidos na íntegra, os quais entraram para o estudo [5][6][7][8][9][10][11][12][13][14][15][16][17] . A Figura 1 sintetiza esquematicamente o processo de pesquisa e análise dos artigos estudados e a Tabela 1 elenca os 13 artigos selecionados, descrevendo autor, ano, a plataforma na qual ele foi retirado, tipo de estudo, metodologia e conclusão.…”

Section: Resultsunclassified

Mensuração Dos Riscos De Doenças Cardiovasculares Por Meio De Dados Antropométricos: Uma Revisão Narrativa

Fonseca¹,

Carrera²,

Silva³

et al. 2021

CPAQV

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Introdução: Estudos epidemiológicos recentes destacam que a dislipidemia, Hipertensão Arterial Sistêmica (HAS), diabetes mellitus, obesidade, tabagismo e falta de atividade física regular são grandes fatores de risco para as doenças cardiovasculares (DCV), entretanto, a identificação precoce e o controle de forma adequada desses fatores são estratégias eficazes na redução da incidência e da morbimortalidade deste grupo de afecções cardiovasculares. Dentre as formas de identificação dos fatores de risco para as DCV estão os dados antropométricos. Objetivo: Verificar a eficácia dos dados antropométricos como parâmetros preditores para os riscos das DCV. Métodos: Trata-se de um estudo de revisão narrativa da literatura. As bases de dados utilizadas foram SciELO e PubMed, com estudos datados no período de 2016 até 2021. Os descritores utilizados foram cardiovascular diseases e anthropometry. Resultado: Foram encontrados 226 artigos. Após a exclusão dos duplicados e retirada dos artigos que não se encontravam entre 2016 e 2021, obteve-se um total de 22 artigos, os quais passaram por leitura de título e resumo para convergirem em 13 artigos lidos na íntegra, os quais entraram para o estudo. Conclusão: O presente estudo constatou que os dados antropométricos IMC, CC, RCQ, RCE e CP são, de forma geral, indicadores eficazes na identificação e prevenção das DCV. Entretanto, cada indicador apresenta uma dinâmica própria de correlação com as DCV, que depende, sobretudo, do sexo, idade e do tipo de DCV.

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Section: Resultsunclassified

Mensuração Dos Riscos De Doenças Cardiovasculares Por Meio De Dados Antropométricos: Uma Revisão Narrativa

Fonseca¹,

Carrera²,

Silva³

et al. 2021

CPAQV

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“…With the accumulation, integration, and standardization of medical information, as well as the constant improvement of computing power, the potential uses for AI in medicine are growing [40]. AI-assisted diagnosis is a very important medical application field and its application in hypertension has gained attention [22][23][24][25][26][27]. Some studies of AI technologies in the prediction and diagnosis of hypertension or primary hypertension have been published; for instance, a real-time risk prediction model of future 1-year incident essential hypertension using XGBoost has been deployed in Maine, providing inspiration for hypertension and related disease intervention [26].…”

Section: Comparison With Prior Workmentioning

confidence: 99%

“…AI technologies, especially machine learning (ML), have received attention in the diagnosis and treatment of hypertension. However, previous studies were focused on predicting future risks of hypertension and building clinical decision support systems to support early screening and treatment [22][23][24][25][26][27][28][29][30][31]. In addition, there are no relevant published studies on AI model-aided diagnosis of secondary hypertension for detecting etiologies of disease and providing effective treatment.…”

Section: Introductionmentioning

confidence: 99%

An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records

Diao¹,

Huo²,

Yan³

et al. 2021

JMIR Med Inform

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Background Secondary hypertension is a kind of hypertension with a definite etiology and may be cured. Patients with suspected secondary hypertension can benefit from timely detection and treatment and, conversely, will have a higher risk of morbidity and mortality than those with primary hypertension. Objective The aim of this study was to develop and validate machine learning (ML) prediction models of common etiologies in patients with suspected secondary hypertension. Methods The analyzed data set was retrospectively extracted from electronic medical records of patients discharged from Fuwai Hospital between January 1, 2016, and June 30, 2019. A total of 7532 unique patients were included and divided into 2 data sets by time: 6302 patients in 2016-2018 as the training data set for model building and 1230 patients in 2019 as the validation data set for further evaluation. Extreme Gradient Boosting (XGBoost) was adopted to develop 5 models to predict 4 etiologies of secondary hypertension and occurrence of any of them (named as composite outcome), including renovascular hypertension (RVH), primary aldosteronism (PA), thyroid dysfunction, and aortic stenosis. Both univariate logistic analysis and Gini Impurity were used for feature selection. Grid search and 10-fold cross-validation were used to select the optimal hyperparameters for each model. Results Validation of the composite outcome prediction model showed good performance with an area under the receiver-operating characteristic curve (AUC) of 0.924 in the validation data set, while the 4 prediction models of RVH, PA, thyroid dysfunction, and aortic stenosis achieved AUC of 0.938, 0.965, 0.959, and 0.946, respectively, in the validation data set. A total of 79 clinical indicators were identified in all and finally used in our prediction models. The result of subgroup analysis on the composite outcome prediction model demonstrated high discrimination with AUCs all higher than 0.890 among all age groups of adults. Conclusions The ML prediction models in this study showed good performance in detecting 4 etiologies of patients with suspected secondary hypertension; thus, they may potentially facilitate clinical diagnosis decision making of secondary hypertension in an intelligent way.

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“…It has been continuously collected since 1998 [28]. The KNHANES datasets are released for public use within one year of the end of each survey year [29, 30].…”

Section: Experimental Studymentioning

confidence: 99%

Reconstruction error based deep neural networks for coronary heart disease risk prediction

et al. 2019

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Coronary heart disease (CHD) is one of the leading causes of death worldwide; if suffering from CHD and being in its end-stage, the most advanced treatments are required, such as heart surgery and heart transplant. Moreover, it is not easy to diagnose CHD at the earlier stage; hospitals diagnose it based on various types of medical tests. Thus, by predicting high-risk people who are to suffer from CHD, it is significant to reduce the risks of developing CHD. In recent years, some research works have been done using data mining to predict the risk of developing diseases based on medical tests. In this study, we have proposed a reconstruction error (RE) based deep neural networks (DNNs); this approach uses a deep autoencoder (AE) model for estimating RE. Initially, a training dataset is divided into two groups by their RE divergence on the deep AE model that learned from the whole training dataset. Next, two DNN classifiers are trained on each group of datasets separately by combining a RE based new feature with other risk factors to predict the risk of developing CHD. For creating the new feature, we use deep AE model that trained on the only high-risk dataset. We have performed an experiment to prove how the components of our proposed method work together more efficiently. As a result of our experiment, the performance measurements include accuracy, precision, recall, F-measure, and AUC score reached 86.3371%, 91.3716%, 82.9024%, 86.9148%, and 86.6568%, respectively. These results show that the proposed AE-DNNs outperformed regular machine learning-based classifiers for CHD risk prediction.

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Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry

Cited by 25 publications

References 52 publications

Mensuração Dos Riscos De Doenças Cardiovasculares Por Meio De Dados Antropométricos: Uma Revisão Narrativa

Mensuração Dos Riscos De Doenças Cardiovasculares Por Meio De Dados Antropométricos: Uma Revisão Narrativa

An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records

Reconstruction error based deep neural networks for coronary heart disease risk prediction

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