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Comparison of Neural Network and Linear Regression Models in Statistically Predicting Mental and Physical Health Status of Breast Cancer Survivors
Uniformed Services University of the Health Sciences Bethesda United States
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In the U.S., there are currently 13.7 million cancer survivors 38. Many cancer survivors experience problems with post-treatment mental and physical functioning.Although research has identified important contributing factors regarding these problems, traditional predictive statistical modeling accounts for less than half the variance in mental and physical function 16 17 113. The relationship among these factors may be better accounted for by a non-linear modeling approach. The goal of this doctoral study was to determine whether a non-linear, adaptive predictive model demonstrated better model fit, showed greater predictive accuracy, and accounted for a greater contribution of independent variables over a traditional statistical model with regard to mental and physical functioning in post-treatment breast cancer survivors. Using demographic, medical, and clinical variables, linear regression was compared to neural network modeling in predicting mental functioning and physical functioning in a sample of post-treatment breast cancer survivors. Contrary to the a priori hypotheses, the neural network model did not outperform the linear regression model in predicting mental and physical functioning of post-treatment breast cancer survivors. However, both linear regression and neural network modeling identified modifiable variables clinical domains of the Cancer Survivor Profile as important predictors of post-treatment mental and physical functioning, with the neural network confirming the findings of the linear regression models. The neural network model also added to the results of the linear regression by identifying additional important variables age, time since diagnosis that may have a non-linear relationship with mental and physical functioning. These findings may promote a better understanding of post-treatment health status and promote targeted clinical interventions.
APPROVED FOR PUBLIC RELEASE