Open AccessPREDICTION OF HYPERTENSION RISKS WITH FEATURE SELECTION AND XGBOOST
Author(s) -
Peng Yan,
Jing Xu,
Ling Ma,
Jie Wang
Publication year - 2021
Publication title -
journal of mechanics in medicine and biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.236
H-Index - 30
eISSN - 1793-6810
pISSN - 0219-5194
DOI - 10.1142/s0219519421400285
Subject(s) - feature selection , lasso (programming language) , model selection , selection (genetic algorithm) , computer science , scale (ratio) , regression , feature (linguistics) , regression analysis , resistant hypertension , medicine , artificial intelligence , econometrics , machine learning , statistics , blood pressure , mathematics , geography , cartography , philosophy , world wide web , linguistics
There are about 1 billion hypertensives patients on a global scale. Hypertension has become the main cause of shorter lifespan and disability for humans worldwide. In this essay, we constructed a new model based on hybrid feature selection and the standard XGBoost for hypertension detection and prediction. After having successfully utilized Lasso regression to identify hypertension-related factors, we used the standard XGBoost model for hypertension prediction. The result from the experiments conducted on the data from the BRFSS shows that proposed model can achieve 77.2% accuracy and 84.6% AUC, both about 7% higher than that without the nonoptimized model. Our proposed model can not only be used to predict the risk of hypertension, but also provide customers with suggestions on how to lead a healthy lifestyle.