A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data

Martinez-Ríos, Erick Axel; Montesinos, Luis; Alfaro-Ponce, Mariel; Pecchia, Leandro

doi:10.1016/j.bspc.2021.102813

Cited by 61 publications

(25 citation statements)

References 89 publications

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“… 17 Decision trees in particular are a supervised ML method with the capacity to rank the input features based upon their relative importance on the outcome. 18 …”

Section: Introductionmentioning

confidence: 99%

Machine learning integration of multimodal data identifies key features of blood pressure regulation

Louca

Tran²,

Toit³

et al. 2022

eBioMedicine

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“… 17 Decision trees in particular are a supervised ML method with the capacity to rank the input features based upon their relative importance on the outcome. 18 …”

Section: Introductionmentioning

confidence: 99%

Machine learning integration of multimodal data identifies key features of blood pressure regulation

Louca

Tran²,

Toit³

et al. 2022

eBioMedicine

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“…This systematic review article was written using the Google Scholar and Web of Science databases. As several articles reviewed different techniques to predict noninvasive BP values (SBP, DBP, and MAP), we focused on the studies that predicted the full waveforms and used the keywords that can provide studies specific to BP waveform prediction [ 42 , 43 , 44 , 45 , 46 , 47 ]. The web search was restricted to journal and conference articles published until January 2022.…”

Section: Searching Strategymentioning

confidence: 99%

“…Additionally, this search technique considered the variables in the dataset and their relationship to the BP waveform as well as the data processing methods used to assess physiological data. There are several review articles available on the Web of Science and Google Scholar that discuss the methodologies for estimating SBP, DBP, and MAP values without using a cuff [ 42 , 43 , 44 , 45 , 46 , 47 ]. Some studies mentioned two techniques (applanation tonometry and the volume clamp method) along with the cuffless methods [ 48 , 49 , 50 , 51 , 52 , 53 ].…”

Section: Searching Strategymentioning

confidence: 99%

A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform

Athaya

Choi

2022

Sensors

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Accurate estimation of blood pressure (BP) waveforms is critical for ensuring the safety and proper care of patients in intensive care units (ICUs) and for intraoperative hemodynamic monitoring. Normal cuff-based BP measurements can only provide systolic blood pressure (SBP) and diastolic blood pressure (DBP). Alternatively, the BP waveform can be used to estimate a variety of other physiological parameters and provides additional information about the patient’s health. As a result, various techniques are being proposed for accurately estimating the BP waveforms. The purpose of this review is to summarize the current state of knowledge regarding the BP waveform, three methodologies (pressure-based, ultrasound-based, and deep-learning-based) used in noninvasive BP waveform estimation research and the feasibility of employing these strategies at home as well as in ICUs. Additionally, this article will discuss the physical concepts underlying both invasive and noninvasive BP waveform measurements. We will review historical BP waveform measurements, standard clinical procedures, and more recent innovations in noninvasive BP waveform monitoring. Although the technique has not been validated, it is expected that precise, noninvasive BP waveform estimation will be available in the near future due to its enormous potential.

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“…A recent review identified and examined ML techniques in hypertension detection and reported a a lack of studies combining sociodemographic and clinical data with signal processing which could increase model performance ( 16 ). A previous study used ML algorithms for automatic classification of hypertension using personal features but failed to include sociodemographic data ( 17 ).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries

Islam

Talukder

Awal

et al. 2022

Front. Cardiovasc. Med.

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BackgroundHypertension is the most common modifiable risk factor for cardiovascular diseases in South Asia. Machine learning (ML) models have been shown to outperform clinical risk predictions compared to statistical methods, but studies using ML to predict hypertension at the population level are lacking. This study used ML approaches in a dataset of three South Asian countries to predict hypertension and its associated factors and compared the model's performances.MethodsWe conducted a retrospective study using ML analyses to detect hypertension using population-based surveys. We created a single dataset by harmonizing individual-level data from the most recent nationally representative Demographic and Health Survey in Bangladesh, Nepal, and India. The variables included blood pressure (BP), sociodemographic and economic factors, height, weight, hemoglobin, and random blood glucose. Hypertension was defined based on JNC-7 criteria. We applied six common ML-based classifiers: decision tree (DT), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), logistic regression (LR), and linear discriminant analysis (LDA) to predict hypertension and its risk factors.ResultsOf the 8,18,603 participants, 82,748 (10.11%) had hypertension. ML models showed that significant factors for hypertension were age and BMI. Ever measured BP, education, taking medicine to lower BP, and doctor's perception of high BP was also significant but comparatively lower than age and BMI. XGBoost, GBM, LR, and LDA showed the highest accuracy score of 90%, RF and DT achieved 89 and 83%, respectively, to predict hypertension. DT achieved the precision value of 91%, and the rest performed with 90%. XGBoost, GBM, LR, and LDA achieved a recall value of 100%, RF scored 99%, and DT scored 90%. In F1-score, XGBoost, GBM, LR, and LDA scored 95%, while RF scored 94%, and DT scored 90%. All the algorithms performed with good and small log loss values <6%.ConclusionML models performed well to predict hypertension and its associated factors in South Asians. When employed on an open-source platform, these models are scalable to millions of people and might help individuals self-screen for hypertension at an early stage. Future studies incorporating biochemical markers are needed to improve the ML algorithms and evaluate them in real life.

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A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data

Cited by 61 publications

References 89 publications

Machine learning integration of multimodal data identifies key features of blood pressure regulation

Machine learning integration of multimodal data identifies key features of blood pressure regulation

A Review of Noninvasive Methodologies to Estimate the Blood Pressure Waveform

Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries

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