Predictive value of different machine learning algorithms for the risk of developing essential hypertension in the elderly
Objective To understand the risk factors of developing essential hypertension in the elderly,and to construct a risk prediction model for developing essential hypertension in the elderly using machine learning algorithms.Methods People with normal blood pressure and no history of hypertension who had a physical examination at the Physical Examination Center,the 305th Hospital of the Chinese People's Liberation Army from January to December 2021 were selected as study subjects,and their blood pressure was observed from January to December 2023,and they were di-vided into the normal blood pressure group(1 553 people)and the new-onset hypertension group(428 cases)according to their blood pressure re-sults.The risk factors of developing essential hypertension in the elderly were analyzed,different machine learning algorithms(random forest,deci-sion tree,support vector machine,K-proximity classification,multi-layer perceptual machine,and logistic regression)were used to construct a pre-dictive model of the risk of developing essential hypertension in the elderly,and the predictive efficacy of the model was assessed by receiver operat-ing characteristic curve.Results There were significant differences in age,white blood cell count,low-density lipoprotein cholesterol,total choles-terol,lymphocyte percentage,globulin,systolic blood pressure,diastolic blood pressure,glycosylated hemoglobin,body mass index,hemoglobin,blood glucose,direct bilirubin,and neutrophils percentage between two groups(P＜0.05).Age,systolic blood pressure,body mass index,and hemoglobin were the risk factors for essential hypertension in the elderly(OR=1.209,1.204,1.243,1.218,P＜0.05).The accuracy,sensitivity,speci-ficity,Jorden index,and area under the curve of random forest prediction model were higher than decision tree,support vector machine,K-proximity classification,multi-layer perceptron machine,logistic regression prediction model.Conclusion Age,systolic blood pressure,body mass index,and hemoglobin were the risk factors of developing essential hypertension in the elderly;the random forest prediction model has better classification effect and discriminative ability among the prediction models for the risk of developing of essential hypertension in the elderly constructed based on the above factors.
ElderlyEssential hypertensionInfluencing factorsMachine learningPredictive model
NETL
NSTL
万方数据
