Genetic prediction of type 2 diabetes using deep neural network

Background Numerous studies have utilized machine-learning techniques to predict the early onset of type 2 diabetes mellitus. However, fewer studies have been conducted to predict an appropriate diagnosis code for the type 2 diabetes mellitus condition. Further, ensemble techniques such as bagging and boosting have likewise been utilized to an even lesser extent. The present study aims to identify appropriate diagnosis codes for type 2 diabetes mellitus patients by means of building a multi-class prediction model which is both parsimonious and possessing minimum features. In addition, the importance of features for predicting diagnose code is provided. Methods This study included 149 patients who have contracted type 2 diabetes mellitus. The sample was collected from a large hospital in Taiwan from November, 2017 to May, 2018. Machine learning algorithms including instance-based, decision trees, deep neural network, and ensemble algorithms were all used to build the predictive models utilized in this study. Average accuracy, area under receiver operating characteristic curve, Matthew correlation coefficient, macro-precision, recall, weighted average of precision and recall, and model process time were subsequently used to assess the performance of the built models. Information gain and gain ratio were used in order to demonstrate feature importance. Results The results showed that most algorithms, except for deep neural network, performed well in terms of all performance indices regardless of either the training or testing dataset that were used. Ten features and their importance to determine the diagnosis code of type 2 diabetes mellitus were identified. Our proposed predictive model can be further developed into a clinical diagnosis support system or integrated into existing healthcare information systems. Both methods of application can effectively support physicians whenever they are diagnosing type 2 diabetes mellitus patients in order to foster better patient-care planning.

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Myocardial infarction in type 2 diabetes using sodium–glucose co-transporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors or glucagon-like peptide-1 receptor agonists: proportional hazards analysis by deep neural network based machine learning

Current Medical Research and Opinion ◽

10.1080/03007995.2019.1706043 ◽

2020 ◽

Vol 36 (3) ◽

pp. 403-409

Author(s):

Tomohide Yamada ◽

Kosuke Iwasaki ◽

Shotaro Maedera ◽

Katsuya Ito ◽

Tomomi Takeshima ◽

...

Keyword(s):

Neural Network ◽

Myocardial Infarction ◽

Type 2 Diabetes ◽

Deep Neural Network ◽

Proportional Hazards ◽

Dipeptidyl Peptidase 4 ◽

Dipeptidyl Peptidase 4 Inhibitors ◽

Glucagon Like Peptide ◽

Glucagon Like Peptide 1

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

Journal of Clinical Medicine ◽

10.3390/jcm7090277 ◽

2018 ◽

Vol 7 (9) ◽

pp. 277 ◽

Cited By ~ 6

Author(s):

Meng-Hsuen Hsieh ◽

Li-Min Sun ◽

Cheng-Li Lin ◽

Meng-Ju Hsieh ◽

Kyle Sun ◽

...

Keyword(s):

Neural Network ◽

Diabetes Mellitus ◽

Colorectal Cancer ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Roc Curve ◽

Deep Neural Network ◽

Severity Index ◽

Increased Risk

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.

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