Research Area: Machine Learning
Ultraviolet radiation (UVR) exposure and family history are major associated risk factors for the development of non-melanoma skin cancer (NMSC). The objective of this study was to develop and validate a multi-parameterized artificial neural network based on available personal health information for early detection of NMSC with high sensitivity and specificity, even in the absence of known UVR exposure and family history. The 1997–2015 NHIS adult survey data used to train and validate our neural network (NN) comprised of 2,056 NMSC and 460,574 non-cancer cases. We extracted 13 parameters for our NN: gender, age, BMI, diabetic status, smoking status, emphysema, asthma, race, Hispanic ethnicity, hypertension, heart diseases, vigorous exercise habits, and history of stroke. This study yielded an area under the ROC curve of 0.81 and 0.81 for training and validation, respectively. Our results (training sensitivity 88.5% and specificity 62.2%, validation sensitivity 86.2% and specificity 62.7%) were comparable to a previous study of basal and squamous cell carcinoma prediction that also included UVR exposure and family history information. These results indicate that our NN is robust enough to make predictions, suggesting that we have identified novel associations and potential predictive parameters of NMSC.
Keywords:
Non-Melanoma Skin Cancer
Multi-Parameterized Artificial Neural Network
Ultraviolet radiation (UVR)
Machine Learning
Deep Learning
Author(s) Name: David Roffman, Gregory Hart, Michael Girardi, Christine J. Ko & Jun Deng
Journal name: Scientific Reports
Conferrence name:
Publisher name: Springer Nature
DOI: 10.1038/s41598-018-19907-9
Volume Information: volume 8, Article number: 1701 (2018)
