Screening for MCI in the Swedish H70 Birth Cohort Study using digital automatic speech biomarker tests for cognition and a machine Learning classifier

Abstract

AbstractBackgroundEven if classic neuropsychological tests often have excellent psychometric properties to detect Mild Cognitive Impairment (MCI), they are not suitable for cost‐effective low‐burden screening at scale. Speech‐based digital biomarkers can be deployed in a highly automated fashion. We present the results of an MCI screening algorithm based on a digital Speech Biomarker for Cognition (SB‐C) in the Swedish H70 birth cohort study.MethodWe used a sample from the Swedish H70 Birth Cohort study (N = 404; 356 cognitively healthy (HC), 48 MCI). We automatically extract the SB‐C score and its subscores (executive function, memory, semantic memory, processing speed) from SVF and RAVLT speech recordings using ki:elements’ proprietary speech analysis pipeline including automatic speech recognition and feature extraction. We performed (1) inferential statistics comparing MCI and HC group based on the biomarker scores and (2) built a machine learning model to screen for MCI. For (1) we performed a non‐parametric Kruskal‐Wallis test to compare SB‐C scores of both HC and MCI groups to check for general feasibility. For (2), we trained a support vector machine model with class weights and leave‐one‐out cross validation to classify between MCI and HC using the SB‐C scores as input (overall score and the subscores).ResultThere was a group difference for the SB‐C aggregated cognition score between the groups (HC > MCI; χ2 = 45.9 (1), p <0.001; Figure 1), and also for the subscores (Table 2). To classify between MCI and HC, using a feature selection method, the best model was found for all the five biomarker scores selected with an Area Under Curve of 0.77 (Figure 2), a specificity of 0.77 and a sensitivity of 0.76 (Table 3).ConclusionWe found that a machine learning‐based screening algorithm based on the SB‐C can detect probable MCI patients in representative population sample of older people using a speech biomarker read‐out.