Deep forest-based hypertension and OSAHS patient screening model

Abstract

Incidence of OSAHS is high in hypertension patients. To make the OSAHS diagnosis more precise and simple, an OSAHS screening model is built hereof by deep forest algorithm with the collected information of hypertension and OSHAS patients from the Sleep and Respiration Centre of a hospital. Firstly, variation in index and dimensions and inter-class imbalance in sample dataset is resolved by normalisation and SMOTE method; and OSAHS screening model is built by deep forest method (gcForest) after redundant information in features is removed with modified chi-square test single feature selection. The results show that with modified chi-square test single feature selection method, the redundant features can be effectively removed and performance of classifier can be improved; deep forest-based OSAHS screening model is superior to other classification models in classification performance and can effectively improve the precision of OSAHS patient screening, reduce the incidence of OSAHS missed diagnosis.