Machine learning-based detection of cognitive decline using SSWTRT: classification performance and decision analysis

Early detection of cognitive decline is essential for preventing dementia progression, yet conventional screening tools such as the Mini-Mental State Examination (MMSE) require trained examiners and substantial time. Building on evidence that dementia is associated with tactile and visual perceptual deficits, this study examined whether the Sound Symbolic Word Texture Recognition Test (SSWTRT)-a rapid, self-administered task using Japanese sound-symbolic words (SSWs)-could identify individuals with suspected cognitive decline through machine learning analysis.

A total of 233 participants diagnosed with idiopathic normal pressure hydrocephalus (mean age = 77.1 ± 7.3 years) completed the SSWTRT, which presents 12 close-up images of material surfaces and requires selecting one of eight SSWs to describe perceived texture. Each response was scored by its concordance with normative data from healthy young adults. Using these 12 item scores, together with participants' age and education, several machine learning classifiers were trained to predict MMSE-based groups (≤27 vs. ≥28). Model performance was evaluated via five-fold cross-validation, and interpretability was examined using SHapley Additive exPlanations (SHAP).

Among the tested models-K-Nearest Neighbors, Random Forest, and Support Vector Machine (SVM)-the balanced SVM achieved the highest performance (accuracy = 0.71, precision = 0.72, recall = 0.72, F1 = 0.72, AUC = 0.72). SHAP analysis revealed that responses to specific images, especially those depicting soft or coarse textures, strongly influenced classification outcomes. Some image items showed effects opposite to the intended scoring direction, indicating possible interference from age-related sensory decline rather than cognitive factors.

These findings demonstrate that machine learning applied to SSWTRT responses can moderately classify individuals with potential cognitive decline using a non-invasive, resource-efficient approach. The model's interpretability analysis highlighted key image features and response tendencies associated with cognitive status, providing guidance for test refinement. Although the current cohort consisted solely of iNPH patients, limiting generalizability, the proposed framework offers a promising foundation for scalable, language-specific cognitive screening tools.