scholarly journals Prediction of Nephropathy in Type 2 Diabetes: An Analysis of the ACCORD Trial Applying Machine Learning Techniques

2019 ◽  
Vol 12 (5) ◽  
pp. 519-528 ◽  
Author(s):  
Violeta Rodriguez‐Romero ◽  
Richard F. Bergstrom ◽  
Brian S. Decker ◽  
Gezim Lahu ◽  
Majid Vakilynejad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Machine Learning Techniques ◽  
Learning Techniques

Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus

2019 ◽  
pp. 198-217
Author(s):  
Angela Pimentel ◽  
Hugo Gamboa ◽  
Isa Maria Almeida ◽  
Pedro Matos ◽  
Rogério T. Ribeiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Health Authorities ◽  
Learning Techniques ◽  
Alternating Decision Tree

Heart diseases and stroke are the number one cause of death and disability among people with type 2 diabetes (T2D). Clinicians and health authorities for many years have expressed interest in identifying individuals at increased risk of coronary heart disease (CHD). Our main objective is to develop a prognostic workflow of CHD in T2D patients using a Holter dataset. This workflow development will be based on machine learning techniques by testing a variety of classifiers and subsequent selection of the best performing system. It will also assess the impact of feature selection and bootstrapping techniques over these systems. Among a variety of classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Alternating Decision Tree (ADT), Random Tree (RT) and K-Nearest Neighbour (KNN), the best performing classifier is NB. We achieved an area under receiver operating characteristics curve (AUC) of 68,06% and 74,33% for a prognosis of 3 and 4 years, respectively.

Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus

2017 ◽  
pp. 89-112
Author(s):  
Angela Pimentel ◽  
Hugo Gamboa ◽  
Isa Maria Almeida ◽  
Pedro Matos ◽  
Rogério T. Ribeiro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Heart Diseases ◽  
Machine Learning Techniques ◽  
Health Authorities ◽  
Learning Techniques ◽  
Alternating Decision Tree

Heart diseases and stroke are the number one cause of death and disability among people with type 2 diabetes (T2D). Clinicians and health authorities for many years have expressed interest in identifying individuals at increased risk of coronary heart disease (CHD). Our main objective is to develop a prognostic workflow of CHD in T2D patients using a Holter dataset.. This workflow development will be based on machine learning techniques by testing a variety of classifiers and subsequent selection of the best performing system. It will also assess the impact of feature selection and bootstrapping techniques over these systems. Among a variety of classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Alternating Decision Tree (ADT), Random Tree (RT) and K-Nearest Neighbour (KNN), the best performing classifier is NB. We achieved an area under receiver operating characteristics curve (AUC) of 68,06% and 74,33% for a prognosis of 3 and 4 years, respectively.

Type 2 diabetes diagnosis assisted by machine learning techniques through the analysis of FTIR spectra of saliva

2021 ◽  
Vol 69 ◽  
pp. 102855
Author(s):  
Miguel Sanchez-Brito ◽  
Francisco J. Luna-Rosas ◽  
Ricardo Mendoza-Gonzalez ◽  
Gustavo J. Vazquez-Zapien ◽  
Julio C. Martinez-Romo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Ftir Spectra ◽  
Machine Learning Techniques ◽  
Diabetes Diagnosis ◽  
Learning Techniques
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