The Use of Machine Learning for the Care of Hypertension and Heart Failure

Cai, Anping; Zhu, Yicheng; Clarkson, Stephen; Feng, Yingqing

doi:10.1016/j.jacasi.2021.07.005

Cited by 15 publications

(15 citation statements)

References 94 publications

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“…In this cross‐sectional, cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES), we observed that a machine‐learning model leveraging both nutritional covariates and demographic covariates could accurately predict hypertension risk (AUROC = 0.84). These match commonly applied risk scores within diet related diseases such as heart disease, with AUROC ranging from 0.6 to 0.88 15,19,35–37 …”

Section: Discussionmentioning

confidence: 99%

“…Machine learning algorithms, which generate predictions from data without explicit direction from the user, can be effectively employed to leverage the ever‐growing data present in hospital systems to augment diagnosis, improve outcomes, and reduce costs 14‐18 . These algorithms can identify hidden patterns within large datasets that may otherwise be missed 19,20 …”

Section: Introductionmentioning

confidence: 99%

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Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

Huang,

Huang

2023

J of Clinical Hypertension

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Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long‐term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine‐learning techniques could accurately predict hypertension risk using nutritional information. A cross‐sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine‐learning model of choice in this study due to its increased performance relative to other common methods within medical studies. Model prediction metrics (e.g., AUROC, Balanced Accuracy) were used to measure overall model efficacy, covariate Gain statistics (percentage each covariate contributes to the overall prediction) and SHapely Additive exPlanations (SHAP, method to visualize each covariate) were used to provide explanations to machine‐learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD = 22.16), 4792 (50%) males, 4858 (50%) female, 3407 (35%) White patients, 2567 (27%) Black patients, 2108 (22%) Hispanic patients, and 981 (10%) Asian patients. From evaluation of model gain statistics, age was found to be the single strongest predictor of hypertension, with a gain of 53.1%. Additionally, demographic factors such as poverty and Black race were also strong predictors of hypertension, with gain of 4.33% and 4.18%, respectively. Nutritional Covariates contributed 37% to the overall prediction: Sodium, Caffeine, Potassium, and Alcohol intake being significantly represented within the model. Machine Learning can be used to predict hypertension.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

Huang,

Huang

2023

J of Clinical Hypertension

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“…Most recently, Ebrahimian and colleagues ( 115 ) found insufficient public information on validating datasets of several Food and Drug Administration (FDA)-regulated imaging-based AI/ML algorithms, recommending more objective data be published to justify clinical use. Cai and colleagues ( 116 ) found ML algorithms and ML-enabled image analysis improved the prediction, diagnosis, and classification of heart failure and hypertension, but further research is needed to investigate these cardiac conditions in terms of management. Other current limitations that exist include the lack of ML standardization to weigh all variables equally, maintain consistent quality, uphold patient safety, and ensure interoperability.…”

Section: Artificial Intelligence Machine Learning and Deep Learningmentioning

confidence: 99%

Recent technologies in cardiac imaging

Counseller

Aboelkassem

2023

Front. Med. Technol.

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Cardiac imaging allows physicians to view the structure and function of the heart to detect various heart abnormalities, ranging from inefficiencies in contraction, regulation of volumetric input and output of blood, deficits in valve function and structure, accumulation of plaque in arteries, and more. Commonly used cardiovascular imaging techniques include x-ray, computed tomography (CT), magnetic resonance imaging (MRI), echocardiogram, and positron emission tomography (PET)/single-photon emission computed tomography (SPECT). More recently, even more tools are at our disposal for investigating the heart’s physiology, performance, structure, and function due to technological advancements. This review study summarizes cardiac imaging techniques with a particular interest in MRI and CT, noting each tool’s origin, benefits, downfalls, clinical application, and advancement of cardiac imaging in the near future.

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“…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

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<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>

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The Use of Machine Learning for the Care of Hypertension and Heart Failure

Cited by 15 publications

References 94 publications

Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

Recent technologies in cardiac imaging

Hypertension prediction using machine learning algorithm among Indonesian adults

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