Title: Deep forest-based hypertension and OSAHS patient screening model

Authors: Ping-Ping Wang; Lei Ma; Yun-Hui Lv; Yan Xiang; Dang-Guo Shao; Xin Xiong

Addresses: Department of Information Engineering and Automation, Kunming University of Science and Technology, No. 727, Jingming South Road, Kunming, Yunnan Province, China ' Department of Information Engineering and Automation, Kunming University of Science and Technology, No. 727, Jingming South Road, Kunming, Yunnan Province, China ' Sleep and Respiration Centre, The First People's Hospital of Yunnan, No. 157, Jinbi Road, Kunming, Yunnan Province, China ' Department of Information Engineering and Automation, Kunming University of Science and Technology, No. 727, Jingming South Road, Kunming, Yunnan Province, China ' Department of Information Engineering and Automation, Kunming University of Science and Technology, No. 727, Jingming South Road, Kunming, Yunnan Province, China ' Department of Information Engineering and Automation, Kunming University of Science and Technology, No. 727, Jingming South Road, Kunming, Yunnan Province, China

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.

Keywords: hypertension; obstructive sleep apnea-hypopnea syndrome; OSAHS; unbalanced data; feature selection; deep forest; screening model.

DOI: 10.1504/IJICT.2020.105606

International Journal of Information and Communication Technology, 2020 Vol.16 No.2, pp.112 - 122

Received: 01 Oct 2018
Accepted: 01 Feb 2019
Published online: 06 Mar 2020 *

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