Machine Learning for the Identification of Biomarker and Risk Factors associated with Depression in Adult Population: Preliminary Results on a Small Cohort

被引:0
作者
Cano-Escalera, Guillermo [1 ,2 ,3 ,4 ]
Graa, Manuel [1 ,2 ]
MacDowell, Karina S. [4 ,6 ]
Leza, Juan C. [4 ,6 ]
Zorilla, Iaki [3 ,4 ,5 ]
Gonzalez-Pinto, Ana [3 ,4 ,5 ]
机构
[1] Univ Basque Country UPV EHU, Fac Comp Sci, Dept Comp Sci & Artificial Intelligence, Paseo Manuel Lardizabal 1, Donostia San Sebastian, Spain
[2] Univ Basque Country UPV EHU, Computat Intelligence Grp, Bilbao, Spain
[3] Hosp Univ Araba, Dept Psychiat, Vitoria, Spain
[4] Biomed Res Ctr Mental Hlth Network CIBERSAM, G10, Vitoria, Spain
[5] Univ Basque Country UPV EHU, Vitoria, Spain
[6] Univ Complutense Madrid UCM, Fac Med, Dept Pharmacol & Toxicol, Madrid, Spain
来源
HYBRID ARTIFICIAL INTELLIGENT SYSTEM, PT I, HAIS 2024 | 2025年 / 14857卷
关键词
Depression; Inflammatory biomarkers; Socio-demographic factors; Adult population; Machine learning; Random forest; Logistic Regression; EPIDEMIOLOGY; PEOPLE;
D O I
10.1007/978-3-031-74183-8_4
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this study we identify some risk factors for depression from the sociodemographic factors and inflammatory biomarkers from a sample of depressed subjects versus a matched group of healthy subjects. The multivariate logistic regression and random forest are used to identify the most significant factors associated with depression. We found significant differences demographic variables corresponding to education, higher levels of education seem to provide protection. Cognitive impairment (SCIP) positive results were also strongly associated with depression. The study also confirmed that married subjects suffer less of depression than separated or divorced subjects. Regarding inflammatory biomarkers, significant differences were obtained in IL6, TNFa, CAT, BDNF, and GSH Total, which indicate a strong association of depression and systemic inflamatory processes.
引用
收藏
页码:41 / 48
页数:8
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