@article{Liu2024, 
author = {Man Liu and Zhuoheng Lv and Hongjing Wang and Lu Liu},
title = {Employing a Backpropagation Neural Network for Predicting Fear of Cancer Recurrence among Non-Small Cell Lung Cancer Patients},
year = {2024},
journal = {Psycho-Oncologie},
volume = {18},
number = {4},
pages = {},
keywords = {Backpropagation neural network, non-small cell lung cancer, cancer recurrence anxiety, predictive analytics},
url = {https://www.sciopen.com/article/10.32604/po.2024.054098},
doi = {10.32604/po.2024.054098},
abstract = {ObjectiveNon-small cell lung cancer (NSCLC) patients often experience significant fear of recurrence. To facilitate precise identification and appropriate management of this fear, this study aimed to compare the efficacy and accuracy of a Backpropagation Neural Network (BPNN) against logistic regression in modeling fear of cancer recurrence prediction.MethodsData from 596 NSCLC patients, collected between September 2023 and December 2023 at the Cancer Hospital of the Chinese Academy of Medical Sciences, were analyzed. Nine clinically and statistically significant variables, identified via univariate logistic regression, were inputted into both BPNN and logistic regression models developed on a training set (N = 427) and validated on an independent set (N = 169). Model performances were assessed using Area Under the Receiver Operating Characteristic (ROC) Curve and Decision Curve Analysis (DCA) in both sets.ResultsThe BPNN model, incorporating nine selected variables, demonstrated superior performance over logistic regression in the training set (AUC = 0.842 vs. 0.711, p < 0.001) and validation set (0.7 vs. 0.675, p < 0.001).ConclusionThe BPNN model outperforms logistic regression in accurately predicting fear of cancer recurrence in NSCLC patients, offering an advanced approach for fear assessment.}
}