scholarly journals Novel Statistical Classification Model of Type 2 Diabetes Mellitus Patients for Tailor-made Prevention Using Data Mining Algorithm.

2002 ◽  
Vol 12 (3) ◽  
pp. 243-248 ◽  
Author(s):  
Koichi Miyaki ◽  
Izumi Takei ◽  
Kenji Watanabe ◽  
Hiroshi Nakashima ◽  
Kiyoaki Watanabe ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Classification Model ◽  
Data Mining Algorithm ◽  
Statistical Classification ◽  
Mining Algorithm ◽  
Using Data

scholarly journals Risk Assessment of Sarcopenia in Patients With Type 2 Diabetes Mellitus Using Data Mining Methods

2020 ◽  
Vol 11 ◽  
Author(s):  
Mengzhao Cui ◽  
Xiaokun Gang ◽  
Fang Gao ◽  
Gang Wang ◽  
Xianchao Xiao ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Type 2 Diabetes ◽  
Risk Assessment ◽  
Type 2 Diabetes Mellitus ◽  
Mining Methods ◽  
Using Data

scholarly journals Comorbidity Study on Type 2 Diabetes Mellitus Using Data Mining

2012 ◽  
Vol 27 (2) ◽  
pp. 197 ◽  
Author(s):  
Hye Soon Kim ◽  
A Mi Shin ◽  
Mi Kyung Kim ◽  
Yoon Nyun Kim
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Using Data

Use of Data Mining in the prediction of risk factors of Type 2 diabetes mellitus in Gulf countries

2021 ◽  
Vol 8 (4) ◽  
pp. 638-645
Author(s):  
W. Boutayeb ◽  
◽  
M. Badaoui ◽  
H. Al Ali ◽  
A. Boutayeb ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Data Mining ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Gulf Coast ◽  
Use Of Data ◽  
Gulf Countries ◽  
Unhealthy Diet

Prevalence of diabetes in Gulf countries is knowing a significant increase because of various risk factors, such as: obesity, unhealthy diet, physical inactivity and smoking. The aim of our proposed study is to use Data Mining and Data Analysis tools in order to determine different risk factors of the development of Type~2 diabetes mellitus (T2DM) in Gulf countries, from Gulf COAST dataset.

scholarly journals A multi-class classification model for supporting the diagnosis of type II diabetes mellitus

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9920
Author(s):  
Kuang-Ming Kuo ◽  
Paul Talley ◽  
YuHsi Kao ◽  
Chi Hsien Huang
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Deep Neural Network ◽  
Classification Model ◽  
Diagnosis Code ◽  
Ensemble Techniques

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.

Socioeconomic Correlates of Inpatient Spending for Patients with Type 2 Diabetes Mellitus in China: Evidence from Hangzhou

2012 ◽  
Vol 120 (01) ◽  
pp. 35-44 ◽  
Author(s):  
H. Li ◽  
B. Chen ◽  
N. Shah ◽  
Z. Wang ◽  
K. Eggleston
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Insurance Coverage ◽  
Cost Effective ◽  
Patient Characteristics ◽  
Artery Disease ◽  
Using Data ◽  
Pharmaceutical Costs

AbstractWe evaluated the factors associated with inpatient costs including total costs, pharmaceutical costs and laboratory costs for diabetes-related admissions.Using data for 960 adult patients admitted between May 2005 and April 2008 with a primary or secondary diagnosis of type 2 diabetes mellitus (DM) at Sir Run Run Shaw Hospital affiliated with Zhejiang University Medical School (SRRSH) in Hangzhou, China, we evaluate the association between patient characteristics and inpatient costs with multivariable regression analyses.Total inpatient costs were positively associated with age, higher UKPDS stroke risk score, and presence of any complication. A regression that included patient socioeconomic and clinical characteristics explained 21.5% of the variation in total inpatient costs; regression estimates indicate that patients with coronary artery disease, retinopathy, nephropathy, neuropathy, and diabetic foot had inpatient costs that were respectively 93.7%, 14.0%, 17.5%, 11.5% and 89.0% higher than otherwise similar patients without those complications. Pharmaceutical costs did not differ by insurance coverage. Insured patients spent 7–16% more on laboratory tests than otherwise similar patients did.Clinical factors, especially presence of diabetes-related complications, appear to be the primary determinants of variation in inpatient costs for patients with type 2 DM in China. To mitigate the health costs increases associated with China’s DM epidemic, policymakers should focus on cost-effective ways to manage patients in outpatient settings to prevent the complications associated with diabetes.

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