In Mathematical biosciences and engineering : MBE
Growing evidence shows that there is an increased risk of cardiovascular diseases among gout patients, especially coronary heart disease (CHD). Screening for CHD in gout patients based on simple clinical factors is still challenging. Here we aim to build a diagnostic model based on machine learning so as to avoid missed diagnoses or over exaggerated examinations as much as possible. Over 300 patient samples collected from Jiangxi Provincial People's Hospital were divided into two groups (gout and gout+CHD). The prediction of CHD in gout patients has thus been modeled as a binary classification problem. A total of eight clinical indicators were selected as features for machine learning classifiers. A combined sampling technique was used to overcome the imbalanced problem in the training dataset. Eight machine learning models were used including logistic regression, decision tree, ensemble learning models (random forest, XGBoost, LightGBM, GBDT), support vector machine (SVM) and neural networks. Our results showed that stepwise logistic regression and SVM achieved more excellent AUC values, while the random forest and XGBoost models achieved more excellent performances in terms of recall and accuracy. Furthermore, several high-risk factors were found to be effective indices in predicting CHD in gout patients, which provide insights into the clinical diagnosis.
Jiang Lili, Chen Sirong, Wu Yuanhui, Zhou Da, Duan Lihua
2023-Jan
** CHD , diagnostic model , gout , imbalance data , machine learning , risk factor selection **
