An machine learning model to predict quality of life subtypes of disabled stroke survivors.

OBJECTIVE

Stroke causes serious physical disability with impaired quality of life (QoL) and heavy burden on health. The goal of this study is to explore the impaired QoL typologies and their predicting factors in physically disabled stroke survivors with machine learning approach.

METHODS

Non-negative matrix factorization (NMF) was applied to clustering 308 physically disabled stroke survivors in rural China based on their responses on the short form 36 (SF-36) assessment of quality of life. Principal component analysis (PCA) was conducted to differentiate the subtypes, and the Boruta algorithm was used to identify the variables relevant to the categorization of two subtypes. A gradient boosting machine(GBM) and local interpretable model-agnostic explanation (LIME) algorithms were used to apply to interpret the variables that drove subtype predictions.

RESULTS

Two distinct subtypes emerged, characterized by short form 36 (SF-36) domains. The feature difference between worsen QoL subtype and better QoL subtype was as follows: role-emotion (RE), body pain (BP) and general health (GH), but not physical function (PF); the most relevant predictors of worsen QoL subtypes were help from others, followed by opportunities for community activity and rehabilitation needs, rather than disability severity or duration since stroke.

INTERPRETATION: The results suggest that the rehabilitation programs should be tailored toward their QoL clustering feature; body pain and emotional-behavioral problems are more crucial than motor deficit; stroke survivors with worsen QoL subtype are most in need of social support, return to community, and rehabilitation.