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

Abstract

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.

Non‐homogeneous continuous‐time Markov chain with covariates: Applications to ambulatory hypertension monitoring

“…Studies have found correlation between hypertension and other factors such as diet, gender, age, and BMI 7‐10 . Some previous studies have modeled hypertension through linear models or machine learning techniques 11‐14 . However, linear models are limited in modeling complex data which does not adhere to a parametric form.…”

Consensus on Recommended Functions of a Smart Home System to Improve Self-Management Behaviors in People With Heart Failure: A Modified Delphi Approach

“…A smart home system with these functions could contribute to evidence gaps outlined in international clinical guidelines, including the need for more data on the effects of fluid restriction, dietary salt restriction and nutrition; the role of remote monitoring; optimal models for follow-up of stable heart failure patients; better definition and classification of patient phenotypes to facilitate improved treatment; and development of better strategies for congestion relief, including monitoring of diuretic administration (5,6). Smart homes can address these gaps by collecting these data directly from patients' home, using machine learning algorithms to create phenotypes, providing automated alerts, remote medication titrations and care (39)(40)(41). The findings may also have implications for technologybased programs for other chronic diseases in which selfmanagement is important (e.g., chronic obstructive pulmonary disease).…”

Machine learning models trained on synthetic datasets of multiple sample sizes for the use of predicting blood pressure from clinical data in a national dataset

“…Other studies have sought to simply predict whether an individual has hypertension or not, using similar methodologies. In these cases, accuracies of predictions have roughly been in the range of 80-90% for high performing models [20,21]. Studies using population level variables such as in this study have typically focussed on predicting the presence or absence of hypertension.…”

“…Indeed, this study represents one of the first studies, if not the first study to predict the actual blood pressure value using descriptive clinical data without the need for EEG or PPG monitoring [10]. Even of those studies which have used descriptive clinical data to predict the presence of hypertension, typically these have used specific variables relating to blood pressure, including doctor's perception of their blood pressure and whether they have measured their blood pressure [21]. Predicting blood pressure is a potentially important research area as it could give rise to targeted public health measures to optimise one of the most well recognised predictors of cardiovascular disease and all-cause mortality [22,23].…”