The role of cognitive function in predicting metabolic risk in schizophrenia
A multi-model comparison incorporating clinical features
Bibliographic Data
| ID | 15516495 |
|---|---|
| Authors | Rui Li (0000-0001-9744-7965), R M J Li (Guangdong Pharmaceutical University), Xuan Ren (0000-0002-1730-3644, Guangdong Pharmaceutical University), Tingyun Jiang (Zhongshan People's Hospital, corresponding author), Jiawen Huo (Guangdong Pharmaceutical University), Junjiao Ping (0009-0001-6454-9444, Zhongshan People's Hospital), Zhu (0000-0002-5395-4743, Guangdong Pharmaceutical University), Shuyi Zhu (0009-0005-8686-185X), Aoxiang Luo (Guangdong Pharmaceutical University, corresponding author) |
| Year | 2026 |
| Volume | 16 |
| Pages | 1724238-1724238 |
| Publication date | 2026-01-12 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2025.1724238 |
| PMID | 41601482 |
| OpenAlex | W7122759484 |
| Language | EN |
| References cited | 72 |
Modeling based on concise clinical and cognitive indicators can effectively achieve metabolic risk stratification in patients with schizophrenia. Rather than relying on a single algorithm, combining the complementary strengths of RF and SVM and selecting models according to specific clinical needs and data characteristics may improve the identification of high-risk individuals and support proactive intervention and management
Cognition · Function (biology · Identification (biology · Intervention (counseling · Risk Stratification · Support vector machine · Dementia and Cognitive Impairment Research · Schizophrenia research and treatment · Tryptophan and brain disorders
Applied Logistic Regression
The Matrics Consensus Cognitive Battery, Part 1
Social Determinants of Cardiovascular Disease
Machine learning algorithm validation with a limited sample size
Support-Vector Networks
A systematic analysis of performance measures for classification tasks
Feature Selection with the Boruta Package
Socioeconomic Status and Cardiovascular Outcomes
Smote
Risk of metabolic syndrome and its components in people with schizophrenia and related psychotic disorders, bipolar disorder and major depressive disorder
Random Forests
A Caution Regarding Rules of Thumb for Variance Inflation Factors
The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia
Relationship between negative symptoms, cognitive function and social function in schizophrenia
Detection of Schizophrenia Cases From Healthy Controls With Combination of Neurocognitive and Electrophysiological Features
Low Physical Activity and Cardiorespiratory Fitness in People With Schizophrenia
Metabolic Syndrome and Cognitive Functions in Schizophrenia—Implementation of Dietary Intervention
Diagnosing schizophrenia with routine blood tests
| Highly cited | No |
|---|