Automatic selection of spoken language biomarkers for dementia detection

This paper analyzes diverse features extracted from spoken language to select the most discriminative ones for dementia detection. We present a two-step feature selection (FS) approach: Step 1 utilizes filter methods to pre-screen features, and Step 2 uses a novel feature ranking (FR) method, referred to as dual dropout ranking (DDR), to rank the screened features and select spoken language biomarkers. The proposed DDR is based on a dual-net architecture that separates FS and dementia detection into two neural networks (namely, the operator and selector). The operator is trained on features obtained from the selector to reduce classification or regression loss. The selector is optimized to predict the operator's performance based on automatic regularization. Results show that the approach significantly reduces feature dimensionality while identifying small feature subsets that achieve comparable or superior performance compared with the full, default feature set. The Python codes are available at https://github.com/kexquan/dual-dropout-ranking.

Automated summary

This paper presents a two-step feature selection approach for dementia detection using diverse features extracted from spoken language. The approach utilizes filter methods for pre-screening features and introduces a novel feature ranking method called dual dropout ranking (DDR) to select the most discriminative features. Experimental results show that the approach significantly reduces feature dimensionality while identifying small feature subsets that achieve comparable or even superior performance compared to the full feature set.