The purpose of this study was to develop a computational method to identify potential predictors for quality of life (QOL) after post stroke rehabilitation.
Five classifiers were trained by five personal factors and nine functional outcome measures by 10-fold cross-validation. The classifier with the highest cross-validated accuracy was considered to be the optimal classifier for QOL prediction.
Particle Swarm-Optimized Support Vector Machine (PSO-SVM) showed highest accuracy in predicting QOL in stroke patients and was adopted as the optimal classifier. Potential predictors were assessed by PSO-SVM with feature selection. The early outcomes of Quality of Movement scale of the Motor Activity Log (MAL_QOM) and the Stroke Impact Scale (SIS) were identified to be the most predictive outcome predictors for QOL.
The approach provides the medical team another possibility to improve the accuracy in predicting QOL in stroke patients. Therapists could determine the therapies for stroke patients more accurately and efficiently to enhance the quality of life after stroke.