Early detection of subjective cognitive decline from self-reported symptoms: An interpretable attention-cost fusion...

Subjective cognitive decline (SCD) refers to self-reported difficulties in concentration, memory, and decision-making. Since SCD is based on subjective experiences, no specific medical test can definitively confirm its presence, making early detection challenging. Thus, it is advantageous to develop an AI model to capitalize on self-reported health complications, personality traits, and sociodemographic factors for early detection of SCD.

This research has proposed an AI-based framework for SCD detection using self-reported measures from the BRFSS 2021 dataset. A novel Weighted Fusion Selection (WFS) approach has been introduced, which combines multiple feature selection techniques to address class imbalance and identify relevant features associated with less frequent classes. The data set has shown a significant imbalance, with individuals at risk of SCD being 81.76% fewer than those not at risk. An Attention Cost Convolutional Neural Network (AC-CNN) has been developed to address this, integrating channel-wise attention mechanisms and cost-sensitive learning to enhance performance across imbalanced data.

The AC-CNN model has achieved a balance between specificity (77%) and sensitivity (81%), with an AUC-ROC of 0.87. This has represented an overall 24.8% improvement in handling class imbalance compared to prior studies. Additional testing on the NHIS 2022 dataset has shown that AC-CNN has maintained balanced performance, confirming its robust generalizability, while other models have remained unstable.

Further, applying SHapley Additive exPlanations (SHAP) explainable techniques to the AC-CNN model has revealed how individual aspects of an individual's health records, lifestyle, and demographics contribute to the prediction of SCD. For example, depression, low education, poor income, inadequate healthcare, and poor overall health have all been strongly linked to an increased risk of SCD.