OBJECTIVE: This study compared the accuracy of artificial neural networks to multiple regression and classification and regression trees in predicting outcomes of 1,644 patients in the Traumatic Brain Injury Model Systems database 1 year after injury. METHODS: Data from rehabilitation admission were used to predict discharge scores on the Functional Independence Measure, the Disability Rating Scale, and the Community Integration Questionnaire. RESULTS: Artificial neural networks did not demonstrate greater accuracy in predicting outcomes than did the more widely used method of multiple regression. Both of these methods outperformed classification and regression trees. CONCLUSION: Because of the sophisticated form of multiple regression with splines that was used, firm conclusions are limited about the relative accuracy of artificial neural networks compared to more widely used forms of multiple regression.
OBJECTIVE: This study compared the accuracy of artificial neural networks to multiple regression and classification and regression trees in predicting outcomes of 1,644 patients in the Traumatic Brain Injury Model Systems database 1 year after injury. METHODS: Data from rehabilitation admission were used to predict discharge scores on the Functional Independence Measure, the Disability Rating Scale, and the Community Integration Questionnaire. RESULTS: Artificial neural networks did not demonstrate greater accuracy in predicting outcomes than did the more widely used method of multiple regression. Both of these methods outperformed classification and regression trees. CONCLUSION: Because of the sophisticated form of multiple regression with splines that was used, firm conclusions are limited about the relative accuracy of artificial neural networks compared to more widely used forms of multiple regression.
Authors: Andrew T Hale; David P Stonko; Jaims Lim; Oscar D Guillamondegui; Chevis N Shannon; Mayur B Patel Journal: J Neurosurg Pediatr Date: 2018-11-02 Impact factor: 2.713