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J Clin Med. 2018 Sep 12;7(9). pii: E277. doi: 10.3390/jcm7090277.

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

Author information

1
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA. emersonhsieh@berkeley.edu.
2
Department of Radiation Oncology, Zuoying Branch of Kaohsiung Armed Forces General Hospital, Kaohsiung 81342, Taiwan. limin.sun@yahoo.com.
3
Management Office for Health Data, China Medical University Hospital, Taichung 40447, Taiwan. orangechengli@gmail.com.
4
College of Medicine, China Medical University, Taichung 40402, Taiwan. orangechengli@gmail.com.
5
Department of Medicine, Poznan University of Medical Sciences, 61-701 Poznań, Poland. 76519@student.ump.edu.pl.
6
Program of Computer Science, Arizona State University, Tempe, AZ 85287, USA. yaomon18@yahoo.com.
7
Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan. hsucy63141@gmail.com.
8
College of Medicine, China Medical University, Taichung 40402, Taiwan. edmundchou@gmail.com.
9
Department of Anesthesiology, China Medical University Hospital, Taichung 40447, Taiwan. edmundchou@gmail.com.
10
Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan. d10040@mail.cmuh.org.tw.
11
Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung 40447, Taiwan. d10040@mail.cmuh.org.tw.
12
Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan. d10040@mail.cmuh.org.tw.

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.

KEYWORDS:

colorectal cancer; deep neural network; receiver operating characteristic; the national health insurance database; type 2 diabetes mellitus

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