A 10-Year Probability Deep Neural Network Prediction Model for Lung Cancer
Department of Computer Science and Information Engineering, Tamkang University, New Taipei 251, Taiwan
National Health Research Institutes, Zhunan 350, Taiwan
Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei 112303, Taiwan
Master Program in Global Health and Development, Taipei Medical University, Taipei 110, Taiwan
Author to whom correspondence should be addressed.
Academic Editor: Ognjen Arandjelovic
Received: 8 January 2021 / Revised: 20 February 2021 / Accepted: 20 February 2021 / Published: 23 February 2021
Cancer is the leading cause of death in Taiwan. Compared with other types of cancer, the incidence of lung cancer is high. In this study, the National Health In-surance Research Database (NHIRDB) was used to determine the diseases and symptoms associ-ated with lung cancer, and a 10-year probability deep neural network prediction model for lung cancer was developed. The proposed model could allow patients with a high risk of lung cancer to receive an earlier diagnosis and support the physicians’ clinical decision-making. As a result, a total of 13 diseases were selected as the predicting factors. The proposed model showed an accuracy of 85.4%, a sensitivity of 72.4% and a specificity of 85%, as well as an 87.4% area under ROC (95%, 0.8604–0.8885) model precision. Based on data analysis and deep learning, our prediction model discovered some features that had not been previously identified by clinical knowledge. This study tracks a decade of clinical diagnostic records to identify possible symptoms and comorbidities of lung cancer, allows early prediction of the disease, and assists more patients with early diagnosis.