In order to better understand the relations between different risk factors in the predisposition to type 2 diabetes, we present a Bayesian Network analysis of a large dataset, composed of three European population studies. Our results show, together with a key role of metabolic syndrome and of glucose after 2 hours of an Oral Glucose Tolerance Test, the importance of education, measured as the number of years of study, in the predisposition to type 2 diabetes.SCOPUS: cp.pinfo:eu-repo/semantics/publishe
Abstract: This paper helps in predicting diabetes by applying data mining technique. The discovery o...
[eng] An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial impo...
Risk factors for type 2 diabetes are multifaceted and interrelated. Unraveling the complex pathways ...
In order to better understand the relations between different risk factors in the predisposition to ...
We propose a novel Bayesian network tool to model the probabilistic relations between a set of type ...
his work presents a tool based on a Dynamic Bayesian Network (DBN) model to simulate the progression...
Objective: Familial dysbetalipoproteinemia (FD) or Type III hyperlipoproteinemia is closely associat...
Background: Determining genetic risk is a fundamental prerequisite for the implementation of primary...
Research into possible risk factors for chronic conditions is a common theme in medical fields. Howe...
Metabolic syndrome is a major factor for cardiovascular disease that can develop into a variety of c...
The insulin sensitivity index () can be used in assessing the risk of developing type 2 diabetes. An...
AIMS/HYPOTHESIS: The aim of this study was to use Mendelian randomisation (MR) to identify the causa...
Type 2 diabetes is a significant health problem because of its high prevalence and strong associatio...
To identify risk factors, neural network analysis is used to create disease prediction models, inclu...
Diabetes mellitus is a chronic disease and a major public health challenge worldwide. Using data min...
Abstract: This paper helps in predicting diabetes by applying data mining technique. The discovery o...
[eng] An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial impo...
Risk factors for type 2 diabetes are multifaceted and interrelated. Unraveling the complex pathways ...
In order to better understand the relations between different risk factors in the predisposition to ...
We propose a novel Bayesian network tool to model the probabilistic relations between a set of type ...
his work presents a tool based on a Dynamic Bayesian Network (DBN) model to simulate the progression...
Objective: Familial dysbetalipoproteinemia (FD) or Type III hyperlipoproteinemia is closely associat...
Background: Determining genetic risk is a fundamental prerequisite for the implementation of primary...
Research into possible risk factors for chronic conditions is a common theme in medical fields. Howe...
Metabolic syndrome is a major factor for cardiovascular disease that can develop into a variety of c...
The insulin sensitivity index () can be used in assessing the risk of developing type 2 diabetes. An...
AIMS/HYPOTHESIS: The aim of this study was to use Mendelian randomisation (MR) to identify the causa...
Type 2 diabetes is a significant health problem because of its high prevalence and strong associatio...
To identify risk factors, neural network analysis is used to create disease prediction models, inclu...
Diabetes mellitus is a chronic disease and a major public health challenge worldwide. Using data min...
Abstract: This paper helps in predicting diabetes by applying data mining technique. The discovery o...
[eng] An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial impo...
Risk factors for type 2 diabetes are multifaceted and interrelated. Unraveling the complex pathways ...