Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms

Abstract

ObjectiveThe prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the risk of hypertension for residents in primary care in Shanghai, China.MethodsA dataset of 40,261 subjects over the age of 35 years was extracted from Electronic Healthcare Records of 47 community health centers from 2017 to 2019 in the Pudong district of Shanghai. Embedded methods were applied for feature selection. Machine learning algorithms, XGBoost, random forest, and logistic regression analyses were adopted in the process of model construction. The performance of models was evaluated by calculating the area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, accuracy and F1-score.ResultsThe XGBoost model outperformed the other two models and achieved an AUC of 0.765 in the testing set. Twenty features were selected to construct the model, including age, diabetes status, urinary protein level, BMI, elderly health self-assessment, creatinine level, systolic blood pressure measured on the upper right arm, waist circumference, smoking status, low-density lipoprotein cholesterol level, high-density lipoprotein cholesterol level, frequency of drinking, glucose level, urea nitrogen level, total cholesterol level, diastolic blood pressure measured on the upper right arm, exercise frequency, time spent engaged in exercise, high salt consumption, and triglyceride level.ConclusionsXGBoost outperformed random forest and logistic regression in predicting the risk of hypertension in primary care. The integration of this risk assessment model into primary care facilities may improve the prevention and management of hypertension in residents.

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“…Common ML algorithms (e.g., XGBoost, Random Forest, Neural Networks) have been found to be more accurate than traditional parametric methods (linear regression, logistic regression) due to their capability to leverage non‐linear and interactive relationships between the independent and dependent variables 2,14,16,17,19,21‐25 . However, the increased predictive accuracy of ML models comes at the cost of interpretability 1,14‐16,19,20 .…”

“…19,20 Common ML algorithms (e.g., XGBoost, Random Forest, Neural Networks) have been found to be more accurate than traditional parametric methods (linear regression, logistic regression) due to their capability to leverage non-linear and interactive relationships between the independent and dependent variables. 2,14,16,17,19,[21][22][23][24][25] However, the increased predictive accuracy of ML models comes at the cost of interpretability. 1,[14][15][16]19,20 Without methods that explain how machine learning algorithms reach their predictions, clinicians will not be able to identify if models are reliable and generalizable or just replicating the biases within the training datasets.…”