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.
Stroke remains a leading cause of death and disability in the developed world . After stroke, the effects of stroke and post stroke rehabilitation are usually assessed by health professional ratings and performance tests [2–4]. However, real life of stroke survivors is affected in multiple ways and may not be described completely by only health and functional status. It is possible that a treatment succeeds in enhancing physical function recovery however induces psychosocial problems [5,6]. In this case, quality of life (QOL) may actually be degraded after poststroke rehabilitation. The WHO suggests that a comprehensive view of quality of life includes not only physical health, but also psychological health, social relationships, and environmental quality . Therefore, to obtain a comprehensive view of the effects after stroke, life quality should also be considered when assessing a person’s health and functioning.
In recent years, assessment of QOL in stroke has become increasingly common. Many recent rehabilitation therapies have been reported to be effective in restoring upper limb motor function after stroke but showed varied effects in QOL [7–10]. Different rehabilitation therapies may benefit different subgroups of the stroke population and cause different effects to QOL. Identifying key predictors of QOL may assist therapists to determine an optimal therapy, which can not only improve physical function but also maximize QOL for a specific subgroup of stroke survivors. Decision making of rehabilitation strategies may be more efficient and complete with identifying predominant predictors of QOL.
Only three studies examined predictors of QOL [5,11,12]. In these three studies, the predictive ability of multiple factors was examined, including demographic factors, vascular risk factors, clinical scales and neuropsychological assessment, and lesion characteristics. However, general predictors of outcomes of QOL were hard to determine because of the heterogeneity among these studies. Both physical and psychological factors were reported to be important in predicting QOL after stroke [5,11,12]. Although stroke rehabilitation gains in QOL are important, the question of which patients may benefit most in QOL from specific therapies has not been widely addressed, and statistical approaches to reveal such associations and predictors may not be optimal [13,14]. However, possible predictors related to QOL performance outcome after rehabilitation remained less discussed. More studies are needed to clarify the predictive ability of diverse QOL predictors in stroke patients.
Practical implementation of outcome predictors in clinical use was also constrained by the complexity of the algorithms. Developing prognostic algorithm based on existing and simple algorithms may reduce the complexity in clinical implementation, increase the use of prognostic model, and further improve the efficiency of rehabilitation therapy. Traditionally, studies examined outcome predictors used regression analysis to discriminate the most predictive factors from others [15–18]. However, the results of regression analysis can only explained the variance of the outcome in percentage. Computational methods can provide another aspect of outcome prediction. The results of regression statistical method showed that the factors were predictors for the outcome measure model, and the model only explained how percentage of the variance in the outcome measure scores. However, the results of computational classifier methods can provide accuracy and more application related to the predictors.
It has been applied in predicting clinical outcome in cancer patients and showed high accuracy and efficiency [19,20]. Using classifiers could improve the accuracy in predicting QOL. Hopefully, predominant predictors could also be better identified. That’s why we try to utilize a computational classifier method to identify potential predictors for quality of life (QOL) after post stroke rehabilitation.