ArticleFrontiers in neuroscience2025
Factors affecting subjective cognitive decline: an automated machine learning approach.
Article in Frontiers in neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: This study aims to develop a screening model for subjective cognitive decline (SCD) based on machine learning techniques. Methods: A retrospective cohort study collected clinical psychological factor data from the "Active Health" screening app under the National Key R&D Program. The final dataset included 598 samples, with an SCD incidence rate of 26.12%. The data were randomly divided into a training set ( Results: The SVM model showed good performance in the training set, with an AUC of 0.82, indicating strong predictive ability. Information Overload (IO), Self-Perception (SP), Energy Level (EL), Depressive Emotion (DE), Gender (SEX), Risk Decision (RD), and Short-Term Memory (STM) were important feature variables for SCD occurrence. Conclusion: This study successfully developed an SVM-based model for screening the risk of SCD. The SVM model demonstrated superior predictive performance compared to Naïve Bayes, Decision Tree, Neural Network, and traditional LR models.
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