Modelling of Hypertension Risk Factors Using Logistic Regression to Prevent Hypertension in Indonesia

Andriani, Putri; Chamidah, Nur

doi:10.1088/1742-6596/1306/1/012027

Cited by 8 publications

(3 citation statements)

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“…Nevertheless, few existing models can be applied to the health management services provided in primary care. The most intractable problem is that most of these models are targeted at patients in a hospital setting ( 6 ); thus, the data input into the models are all extracted from the EHRs of hospitals, which may not be readily available in primary care settings and suitable for general practitioners to implement.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Chen

Feng

Geng

et al. 2022

Front. Public Health

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ObjectiveThe prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the risk of hypertension for residents in primary care in Shanghai, China.MethodsA dataset of 40,261 subjects over the age of 35 years was extracted from Electronic Healthcare Records of 47 community health centers from 2017 to 2019 in the Pudong district of Shanghai. Embedded methods were applied for feature selection. Machine learning algorithms, XGBoost, random forest, and logistic regression analyses were adopted in the process of model construction. The performance of models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, accuracy and F1-score.ResultsThe XGBoost model outperformed the other two models and achieved an AUC of 0.765 in the testing set. Twenty features were selected to construct the model, including age, diabetes status, urinary protein level, BMI, elderly health self-assessment, creatinine level, systolic blood pressure measured on the upper right arm, waist circumference, smoking status, low-density lipoprotein cholesterol level, high-density lipoprotein cholesterol level, frequency of drinking, glucose level, urea nitrogen level, total cholesterol level, diastolic blood pressure measured on the upper right arm, exercise frequency, time spent engaged in exercise, high salt consumption, and triglyceride level.ConclusionsXGBoost outperformed random forest and logistic regression in predicting the risk of hypertension in primary care. The integration of this risk assessment model into primary care facilities may improve the prevention and management of hypertension in residents.

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Section: Introductionmentioning

confidence: 99%

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Chen

Feng

Geng

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Nevertheless, few existing models can be applied to the health management services provided in primary care. The most intractable problem is that most of these models are targeted at patients in a hospital setting 5 ; thus, the data input into the models are all extracted from the EHRs of hospitals, which may not be easily available in primary care and suitable for general practitioners to put into use.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Risk of Hypertension in Residents in Primary Care in Shanghai, China with Machine Learning Algorithms

Chen

Fan

Geng

et al. 2022

Preprint

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The prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the risk of hypertension for residents in primary care in Shanghai, China. A dataset of 40,261 subjects over the age of 35 years was extracted from Electronic Healthcare Records of 47 community health centres from 2017 to 2019 in the Pudong district of Shanghai. The XGBoost model outperformed the other two models and achieved an AUC of 0.765 in the testing set. Twenty features were selected to construct the model, including age, diabetes status, urinary protein, BMI, elderly health self-assessment, creatinine, systolic blood pressure of the upper right arm, waist circumference, smoking status, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, frequency of drinking, glucose, urea nitrogen, total cholesterol, diastolic blood pressure of the upper right arm, exercise frequency, time spent engaged in exercise, high salt consumption, and triglycerides. XGBoost outperformed random forest and logistic regression in predicting the risk of hypertension in primary care. Integration of such a risk assessment model into primary care may improve the prevention and management of hypertension in residents.

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“…Um dos exemplos mais famosos de utilização da técnica é o Framingham Heart Study, um estudo sobre fatores que podem ocasionar doenças cardiovasculares, realizado com a parceria da Universidade de Boston (Mesquita, 2014). Atualmente, a regressão logística tem sido utilizada em diversas áreas da pesquisa científica, como saúde (Ahmad et al, 2014;Heo & Ryu, 2018;Andriani & Chamidah, 2019;Johnson et al, 2010), economia (Cruz & Mapa, 2013), marketing e educação (Bozpolat, 2016;Constantin, 2015;Koç & Yeniaj, 2013), sendo considerada uma importante ferramenta para análise de variáveis dicotômicas.…”

Section: Introductionunclassified

Aplicação da regressão logística na análise dos dados dos fatores de risco associados à hipertensão arterial

Silveira

Barbosa

Peixoto

et al. 2021

RSD

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A regressão logística é uma técnica importante para modelagem de dados quando se deseja analisar a relação entre uma variável resposta e uma ou mais variáveis independentes. A técnica permite que se estime as chances relacionadas à probabilidade da ocorrência de um evento de interesse. A regressão logística diferencia-se da regressão linear devido à natureza dicotômica da variável dependente e vem sendo utilizada em diversas áreas do conhecimento, incluindo estudos na área da saúde. O presente trabalho utilizou a técnica da regressão logística com o objetivo de analisar a associação entre Hipertensão Arterial e determinados fatores de risco. Os dados utilizados provém da Pesquisa Nacional de Saúde (PNS) do ano de 2019, realizada pelo Instituto Brasileiro de Geografia e Estatística (IBGE) em território nacional. Foram ajustados dois modelos, sendo o modelo final composto por sete variáveis com significância estatística de 5%. As técnicas de diagnóstico indicaram um ajuste adequado do modelo, bem como sua precisão para predições. Os resultados apontam que fatores como o aumento da idade, índice de massa corporal (IMC) alto e o diagnóstico positivo para diabetes aumentam as chances de um indivíduo ser hipertenso.

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Modelling of Hypertension Risk Factors Using Logistic Regression to Prevent Hypertension in Indonesia

Cited by 8 publications

References 1 publication

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Evaluating the Risk of Hypertension in Residents in Primary Care in Shanghai, China with Machine Learning Algorithms

Aplicação da regressão logística na análise dos dados dos fatores de risco associados à hipertensão arterial

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