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Predicting increased blood pressure using machine learning.

ABSTRACT: The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudo R (2) (.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudo R (2) (.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

SUBMITTER: Golino HF

PROVIDER: S-EPMC3941962 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Predicting increased blood pressure using machine learning.

Golino Hudson Fernandes HF Amaral Liliany Souza de Brito LS Duarte Stenio Fernando Pimentel SF Gomes Cristiano Mauro Assis CM Soares Telma de Jesus Tde J Dos Reis Luciana Araujo LA Santos Joselito J

Journal of obesity 20140123

The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combinati ...[more]

PMID: 24669313

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Project description:BackgroundRepeated invasive intracranial pressure (ICP) monitoring is desirable because many neurosurgical pathologies are associated with elevated ICP. On the other hand, it could become a risk for children to repeat sedation, anesthesia, or radiation exposure. As a non-invasive method, measurements of optic nerve sheath diameter (ONSD) has been revealed to accurately predict increased ICP. However, no studies have indicated a relationship among age, brain, and ventricular parameters in normal children, nor a prediction of increased ICP with artificial intelligence.Methods and findingsThis study enrolled 400 normal children for control and 75 children with signs of increased ICP between 2009 and 2019. Measurements of the parameters including ONSD on CT were obtained. A supervised machine learning was applied to predict suspected increased ICP based on CT measurements. A linear correlation was shown between ln(age) and mean ONSD (mONSD) in normal children, revealing mONSD = 0.36ln(age)+2.26 (R2 = 0.60). This study revealed a linear correlation of mONSD measured on CT with ln(age) and the width of the brain, not the width of the ventricles in 400 normal children based on the univariate analyses. Additionally, the multivariate analyses revealed minimum bicaudate nuclei distance was also associated with mONSD. The results of the group comparison between control and suspected increased ICP revealed a statistical significance in mONSD and the width of the ventricles. The study indicated that supervised machine learning application could be applied to predict suspected increased ICP in children, with an accuracy of 94% for training, 91% for test.ConclusionsThis study clarified three issues regarding ONSD and ICP. Mean ONSD measured on CT was correlated with ln(age) and the width of the brain, not the width of the ventricles in 400 normal children based on the univariate analyses. The multivariate analyses revealed minimum bicaudate nuclei distance was also associated with mONSD. Mean ONSD and the width of ventricles were statistically significant in children with signs of elevated ICP. Finally, the study showed that machine learning could be used to predict children with suspected increased ICP.

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Predicting increased blood pressure using machine learning.

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Predicting increased blood pressure using machine learning.

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