Prediction and causal inference of cardiovascular and cerebrovascular diseases based on lifestyle questionnaires.

Autor: Nambo R; School of Electrical Information Communication Engineering, College of Science and Engineering, Kanazawa University, Kanazawa, Japan., Karashima S; Institute of Liberal Arts and Science, Kanazawa University, Kanazawa, Japan. skarashima@staff.kanazawa-u.ac.jp., Mizoguchi R; Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan., Konishi S; Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan., Hashimoto A; Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan., Aono D; Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan., Kometani M; Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan., Furukawa K; Health Care Center, Japan Advanced Institute of Science and Technology, Nomi, Japan., Yoneda T; Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan., Imamura K; Faculty of Electrical, Information and Communication Engineering, Institute of Science and Engineering, Kanazawa University, Kanazawa, Japan., Nambo H; Institute of Transdisciplinary Sciences, Kanazawa University, Kanazawa, Japan. nambo@blitz.ec.t.kanazawa-u.ac.jp.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 May 07; Vol. 14 (1), pp. 10492. Date of Electronic Publication: 2024 May 07.
DOI: 10.1038/s41598-024-61047-w
Abstrakt: Cardiovascular and cerebrovascular diseases (CCVD) are prominent mortality causes in Japan, necessitating effective preventative measures, early diagnosis, and treatment to mitigate their impact. A diagnostic model was developed to identify patients with ischemic heart disease (IHD), stroke, or both, using specific health examination data. Lifestyle habits affecting CCVD development were analyzed using five causal inference methods. This study included 473,734 patients aged ≥ 40 years who underwent specific health examinations in Kanazawa, Japan between 2009 and 2018 to collect data on basic physical information, lifestyle habits, and laboratory parameters such as diabetes, lipid metabolism, renal function, and liver function. Four machine learning algorithms were used: Random Forest, Logistic regression, Light Gradient Boosting Machine, and eXtreme-Gradient-Boosting (XGBoost). The XGBoost model exhibited superior area under the curve (AUC), with mean values of 0.770 (± 0.003), 0.758 (± 0.003), and 0.845 (± 0.005) for stroke, IHD, and CCVD, respectively. The results of the five causal inference analyses were summarized, and lifestyle behavior changes were observed after the onset of CCVD. A causal relationship from 'reduced mastication' to 'weight gain' was found for all causal species theory methods. This prediction algorithm can screen for asymptomatic myocardial ischemia and stroke. By selecting high-risk patients suspected of having CCVD, resources can be used more efficiently for secondary testing.
(© 2024. The Author(s).)
Databáze: MEDLINE
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