Open AccessPrediction Model of Hypertension Complications Based on GBDT and LightGBM
Author(s) -
Xinpeng Ji,
Wenbing Chang,
Yue Zhang,
Houxiang Liu,
Chen Bang,
Yiyong Xiao,
Shenghan Zhou
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1813/1/012008
Subject(s) - medicine , feature selection , computer science , stroke (engine) , artificial intelligence , machine learning , cardiology , mechanical engineering , engineering
Complications caused by hypertension include heart failure, stroke, arteriosclerosis, etc. The prediction of hypertension complications is a hot issue, and it is difficult to predict it from a medical perspective. In this study, we aim to establish a prediction model of hypertension complications based on machine learning and data mining. We first proposed a GBDT-based feature selection method, which can screen out medical indicators that affect the hypertension complications. On this basis, we established a hypertension complications prediction model based on LightGBM. The results show that after 10-fold cross-validation and comparison analysis, the accuracy, F1 and AUC of the prediction model are 0.9189, 0.8888, and 0.9233 respectively, which are significantly better than other machine learning models. Therefore, the proposed method can accurately predict hypertension complications, so as to provide effective clinical auxiliary diagnosis for doctors and help them take preventive measures to reduce the impact of hypertension complications.