Next Article in Journal
Effects of 462 nm Light-Emitting Diode on the Inactivation of Escherichia coli and a Multidrug-Resistant by Tetracycline Photoreaction
Previous Article in Journal
Regeneration of Transected Recurrent Laryngeal Nerve Using Hybrid-Transplantation of Skeletal Muscle-Derived Stem Cells and Bioabsorbable Scaffold
Previous Article in Special Issue
Short Course of Insulin Treatment versus Metformin in Newly Diagnosed Patients with Type 2 Diabetes
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
J. Clin. Med. 2018, 7(9), 277; https://doi.org/10.3390/jcm7090277

Development of a Prediction Model for Colorectal Cancer among Patients with Type 2 Diabetes Mellitus Using a Deep Neural Network

1
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
2
Department of Radiation Oncology, Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung 81342, Taiwan
3
Management Office for Health Data, China Medical University Hospital, Taichung 40447, Taiwan
4
College of Medicine, China Medical University, Taichung 40402, Taiwan
5
Department of Medicine, Poznan University of Medical Sciences, 61-701 Poznań, Poland
6
Program of Computer Science, Arizona State University, Tempe, AZ 85287, USA
7
Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
8
Department of Anesthesiology, China Medical University Hospital, Taichung 40447, Taiwan
9
Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 40447, Taiwan
10
Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
Equally contributed to this article.
*
Author to whom correspondence should be addressed.
Received: 21 August 2018 / Revised: 8 September 2018 / Accepted: 10 September 2018 / Published: 12 September 2018
(This article belongs to the Special Issue Type 2 Diabetes: Update on Pathophysiology and Treatment)
Full-Text   |   PDF [550 KB, uploaded 13 September 2018]   |  

Abstract

Objectives: Observational studies suggested that patients with type 2 diabetes mellitus (T2DM) presented a higher risk of developing colorectal cancer (CRC). The current study aims to create a deep neural network (DNN) to predict the onset of CRC for patients with T2DM. Methods: We employed the national health insurance database of Taiwan to create predictive models for detecting an increased risk of subsequent CRC development in T2DM patients in Taiwan. We identified a total of 1,349,640 patients between 2000 and 2012 with newly diagnosed T2DM. All the available possible risk factors for CRC were also included in the analyses. The data were split into training and test sets with 97.5% of the patients in the training set and 2.5% of the patients in the test set. The deep neural network (DNN) model was optimized using Adam with Nesterov’s accelerated gradient descent. The recall, precision, F1 values, and the area under the receiver operating characteristic (ROC) curve were used to evaluate predictor performance. Results: The F1, precision, and recall values of the DNN model across all data were 0.931, 0.982, and 0.889, respectively. The area under the ROC curve of the DNN model across all data was 0.738, compared to the ideal value of 1. The metrics indicate that the DNN model appropriately predicted CRC. In contrast, a single variable predictor using adapted the Diabetes Complication Severity Index showed poorer performance compared to the DNN model. Conclusions: Our results indicated that the DNN model is an appropriate tool to predict CRC risk in patients with T2DM in Taiwan. View Full-Text
Keywords: type 2 diabetes mellitus; colorectal cancer; deep neural network; the national health insurance database; receiver operating characteristic type 2 diabetes mellitus; colorectal cancer; deep neural network; the national health insurance database; receiver operating characteristic
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Hsieh, M.-H.; Sun, L.-M.; Lin, C.-L.; Hsieh, M.-J.; Sun, K.; Hsu, C.-Y.; Chou, A.-K.; Kao, C.-H. Development of a Prediction Model for Colorectal Cancer among Patients with Type 2 Diabetes Mellitus Using a Deep Neural Network. J. Clin. Med. 2018, 7, 277.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Clin. Med. EISSN 2077-0383 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top