Predicting Hypertension Based on Machine Learning Methods: A Case Study in Northwest Vietnam

Hypertension is a major risk factor for cardiovascular diseases (CVD). Identifying the persons at these high risks plays an important role because it would save time and money before using any complex, invasive and/or expensive diagnostic methods. This task can be partly dealt with the help of advanced machine-learning methods. Specifically, a prediction model can be developed based on some indicator factors of the people at high risks which are easily-obtained, non-invasive and low-cost. This paper presents the first work towards predicting hypertension risks based on of the people in the Northwest region of Vietnam where hypertension rate is increasing. 2.509 samples were collected and classified into two levels of hypertension or no hypertension. We investigated and compared the performance of robust machine learning methods including single classifiers such as Naïve Bayes, MLP, Decision Tree, kNN and SVM; and ensemble classifiers such as bagging, boosting and voting methods, to generate mathematical models to predict the risk of hypertension disease. The experimental results showed that the best random forest model yielded 72.39% in the F1 score. This result was quite promising and can be applied in Vietnamese hospital nowadays.