Estimating cardiovascular mortality in patients with hypertension using machine learning: The role of depression classification based on lifestyle and physical activity.

Journal: Journal of psychosomatic research
PMID: 39752763

Abstract

PURPOSE: This study aims to harness machine learning techniques, particularly the Random Survival Forest (RSF) model, to assess the impact of depression on cardiovascular disease (CVD) mortality among hypertensive patients. A key objective is to elucidate the interplay between mental health, lifestyle, and physical activity while comparing the effectiveness of the RSF model against the traditional Cox proportional hazards model in predicting CVD mortality.

Authors

  • Xingyu Liu
    First People's Hospital of Zunyi City, Zunyi, China.
  • Zeyu Luo
    Chongqing Key Laboratory of Vector Insects.
  • Fengshi Jing
    Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China.
  • Hao Ren
    Department of Rheumatology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China. Electronic address: renhao67@aliyun.com.
  • Changjin Li
    Faculty of Data Science, City University of Macau, Taipa 999078, Macao SAR, China.
  • Lei Wang
    Department of Nursing, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Tao Chen
    School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.

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