Next Article in Journal
Germline and Somatic Whole-Exome Sequencing Identifies New Candidate Genes Involved in Familial Predisposition to Serrated Polyposis Syndrome
Previous Article in Journal
Clinical Perspectives in Addressing Unsolved Issues in (Neo)Adjuvant Therapy for Primary Breast Cancer
Previous Article in Special Issue
Deep Learning Based HPV Status Prediction for Oropharyngeal Cancer Patients
Open AccessArticle

A 10-Year Probability Deep Neural Network Prediction Model for Lung Cancer

by 1,2,3, 1 and 3,4,*
1
Department of Computer Science and Information Engineering, Tamkang University, New Taipei 251, Taiwan
2
National Health Research Institutes, Zhunan 350, Taiwan
3
Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei 112303, Taiwan
4
Master Program in Global Health and Development, Taipei Medical University, Taipei 110, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Ognjen Arandjelovic
Cancers 2021, 13(4), 928; https://doi.org/10.3390/cancers13040928
Received: 8 January 2021 / Revised: 20 February 2021 / Accepted: 20 February 2021 / Published: 23 February 2021
(This article belongs to the Special Issue Machine Learning Techniques in Cancer)
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.
Cancer is the leading cause of death in Taiwan. According to the Cancer Registration Report of Taiwan’s Ministry of Health and Welfare, a total of 13,488 people suffered from lung cancer in 2016, making it the second-most common cancer and the leading cancer in men. Compared with other types of cancer, the incidence of lung cancer is high. In this study, the National Health Insurance Research Database (NHIRDB) was used to determine the diseases and symptoms associated 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. The study was designed as a cohort study. The subjects were patients who were diagnosed with lung cancer between 2000 and 2009, and the patients’ disease histories were back-tracked for a period, extending to ten years before the diagnosis of lung cancer. As a result, a total of 13 diseases were selected as the predicting factors. A nine layers deep neural network model was created to predict the probability of lung cancer, depending on the different pre-diagnosed diseases, and to benefit the earlier detection of lung cancer in potential patients. The model is trained 1000 times, the batch size is set to 100, the SGD (Stochastic gradient descent) optimizer is used, the learning rate is set to 0.1, and the momentum is set to 0.1. 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 (AUROC) (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. View Full-Text
Keywords: lung cancer; prediction model; early diagnosis; health prevention; machine learning; deep neural network model lung cancer; prediction model; early diagnosis; health prevention; machine learning; deep neural network model
Show Figures

Figure 1

MDPI and ACS Style

Lee, H.-A.; Chao, L.R.; Hsu, C.-Y. A 10-Year Probability Deep Neural Network Prediction Model for Lung Cancer. Cancers 2021, 13, 928. https://doi.org/10.3390/cancers13040928

AMA Style

Lee H-A, Chao LR, Hsu C-Y. A 10-Year Probability Deep Neural Network Prediction Model for Lung Cancer. Cancers. 2021; 13(4):928. https://doi.org/10.3390/cancers13040928

Chicago/Turabian Style

Lee, Hsiu-An; Chao, Louis R.; Hsu, Chien-Yeh. 2021. "A 10-Year Probability Deep Neural Network Prediction Model for Lung Cancer" Cancers 13, no. 4: 928. https://doi.org/10.3390/cancers13040928

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop