Machine learning and blood pressure

Santhanam, Prasanna; Ahima, Rexford S.

doi:10.1111/jch.13700

Cited by 16 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[24][25][26][27][28] They have also been used for treating cardiovascular diseases. [29][30][31][32][33] MLT's can be proposed as good candidates to identify different material model parameters, and we strongly believe that the use of these mathematical tools could successfully help to improve the characterization of soft biological tissues. Moreover, the use of MLTs also presents certain advantage in terms of computational costs, reducing computation time in comparison to gradient-base methods, where this time becomes indefinite, searching for an appropriate initial seed.…”

Section: Which Includes Microstructural Information In the Model By Mmentioning

confidence: 99%

Untitled

2020

IJBSBE

View full text Add to dashboard Cite

Section: Which Includes Microstructural Information In the Model By Mmentioning

confidence: 99%

Untitled

2020

IJBSBE

View full text Add to dashboard Cite

“…However, as a medical condition, high blood pressure is influenced by several factors, both demographic and lifestyle factors [9]. Risk factors such as age, sex, family history, smoking habit, alcohol consumption, body mass index, waist circumference, hip circumference, and waist-hip ratio are among the most practical and cost-effective measures for predicting cardiovascular risk as well as hypertension [9]- [11]. Prevention of hypertension and its complications has long been a subject in the public health domain.…”

Section: Introductionmentioning

confidence: 99%

“…ML approaches to predict and classify health outcomes are increasingly used in the health sector. ML as a part of artificial intelligence (AI) is gaining immense attention in the management of chronic disease and is considered a promising alternative to traditional methods for clinical predictions [11], [18], [19]. Therefore, developing a hypertension prediction model using a ML approach is necessary.…”

Section: Introductionmentioning

confidence: 99%

Hypertension prediction using machine learning algorithm among Indonesian adults

Kurniawan

Utomo

Siregar

et al. 2023

IJ-AI

View full text Add to dashboard Cite

<div><div><p>Early risk prediction and appropriate treatment are believed to be able to delay the occurrence of hypertension and attendant conditions. Many hypertension prediction models have been developed across the world, but they cannot be generalized directly to all populations, including for Indonesian population. This study aimed to develop and validate a hypertension risk-prediction model using machine learning (ML). The modifiable risk factors are used as the predictor, while the target variable on the algorithm is hypertension status. This study compared several machine-learning algorithms such as decision tree, random forest, gradient boosting, and logistic regression to develop a hypertension prediction model. Several parameters, including the area under the receiver operator characteristic curve (AUC), classification accuracy (CA), F1 score, precision, and recall were used to evaluate the models. Most of the predictors used in this study were significantly correlated with hypertension. Logistic regression algorithm showed better parameter values, with AUC 0.829, CA 89.6%, recall 0.896, precision 0.878, and F1 score 0.877. ML offers the ability to develop a quick prediction model for hypertension screening using non-invasive factors. From this study, we estimate that 89.6% of people with elevated blood pressure obtained on home blood pressure measurement will show clinical hypertension.</p></div></div>

show abstract

“…Recently, as a new strategy for blood pressure management, the importance of predicting blood pressure by using machine learning (ML) of big data-based artificial intelligence (AI) methods has been highlighted [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm

Kim

Hwang

Lee

et al. 2022

IJERPH

View full text Add to dashboard Cite

Few studies classified and predicted hypertension using blood pressure (BP)-related determinants in a deep learning algorithm. The objective of this study is to develop a deep learning algorithm for the classification and prediction of hypertension with BP-related factors based on the Korean Genome and Epidemiology Study-Ansan and Ansung baseline survey. We also investigated whether energy intake adjustment is adequate for deep learning algorithms. We constructed a deep neural network (DNN) in which the number of hidden layers and the number of nodes in each hidden layer are experimentally selected, and we trained the DNN to diagnose hypertension using the dataset while varying the energy intake adjustment method in four ways. For comparison, we trained a decision tree in the same way. Experimental results showed that the DNN performs better than the decision tree in all aspects, such as having higher sensitivity, specificity, F1-score, and accuracy. In addition, we found that unlike general machine learning algorithms, including the decision tree, the DNNs perform best when energy intake is not adjusted. The result indicates that energy intake adjustment is not required when using a deep learning algorithm to classify and predict hypertension with BP-related factors.

show abstract

Machine learning and blood pressure

Cited by 16 publications

References 13 publications

Untitled

Untitled

Hypertension prediction using machine learning algorithm among Indonesian adults

Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm

Contact Info

Product

Resources

About