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

Sheikh Mohammed Shariful Islam; Ashis Talukder; Md Abdul Awal; Md Muhammad Umer Siddiqui; Md Martuza Ahamad; Benojir Ahammed; Lal B Rawal; Roohallah Alizadehsani; Jemal Abawajy; Liliana Laranjo; Clara K Chow; Ralph Maddison

doi:10.3389/fcvm.2022.839379

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

Front Cardiovasc Med. 2022 Mar 31:9:839379. doi: 10.3389/fcvm.2022.839379. eCollection 2022.

Affiliations

¹ Institute for Physical Activity and Nutrition, Faculty of Health, Deakin University, Melbourne, VIC, Australia.
² Statistics Discipline, Khulna University, Khulna, Bangladesh.
³ Electronics and Communication Engineering Discipline, Khulna University, Khulna, Bangladesh.
⁴ Department of Hospital Medicine, Thomas Jefferson University, Philadelphia, PA, United States.
⁵ Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj, Bangladesh.
⁶ School of Health Medical and Applied Sciences, Central Queensland University, Sydney, NSW, Australia.
⁷ Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC, Australia.
⁸ School of Information Technology, Deakin University, Geelong, VIC, Australia.
⁹ Faculty of Medicine and Health, Westmead Applied Research Centre, The University of Sydney, Sydney, NSW, Australia.

Abstract

Background: Hypertension 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.

Methods: We 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.

Results: Of 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%.

Conclusion: ML 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.

Keywords: Demographic and Health Survey; South Asia; algorithms; artificial intelligence; blood pressure; cardiovascular diseases; risk factors.