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

• DOI: 10.3389/fcvm.2022.839379
• Publication Date: 2022-03-31T06:35:41Z
• Authors: Sheikh Mohammed S...; Ashis Talukder; Md. Abdul Awal; Md. Muhammad Umer...; Md. Martuza Ahamad; Benojir Ahammed; Lal B. Rawal; Roohallah Alizade...; Jemal Abawajy; Liliana Laranjo; Clara K. Chow; Ralph Maddison
• Keywords: Promotion and Disease Reduction in Cardiovascular Health; Artificial intelligence; Population; Cardiology; Health Professions; South Asia; Cardiovascular Medicine; algorithms; 03 medical and health sciences; 0302 clinical medicine; Health Information Management; Health Sciences; risk factors; Diseases of the circulatory (Cardiovascular) system; Internal medicine; Machine Learning in Healthcare and Medicine; blood pressure; Management of Hypertension and Cardiovascular Risk Factors; artificial intelligence; Computer science; Demographic and Health Survey; 3. Good health; Environmental health; RC666-701; Hypertension; Medicine; Heart Disease Prediction; Cardiology and Cardiovascular Medicine

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.