Learning ensemble classifiers for diabetic retinopathy assessment. 2018

Emran Saleh, and Jerzy Błaszczyński, and Antonio Moreno, and Aida Valls, and Pedro Romero-Aroca, and Sofia de la Riva-Fernández, and Roman Słowiński
Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain. Electronic address: emran.saleh@urv.cat.

Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doctors to determine the risk of each patient to attain this condition, so that patients with a low risk may be screened less frequently and the use of resources can be improved. This paper explores the use of two kinds of ensemble classifiers learned from data: fuzzy random forest and dominance-based rough set balanced rule ensemble. These classifiers use a small set of attributes which represent main risk factors to determine whether a patient is in risk of developing diabetic retinopathy. The levels of specificity and sensitivity obtained in the presented study are over 80%. This study is thus a first successful step towards the construction of a personalized decision support system that could help physicians in daily clinical practice.

UI MeSH Term Description Entries
D011237 Predictive Value of Tests In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test. Negative Predictive Value,Positive Predictive Value,Predictive Value Of Test,Predictive Values Of Tests,Negative Predictive Values,Positive Predictive Values,Predictive Value, Negative,Predictive Value, Positive
D011379 Prognosis A prediction of the probable outcome of a disease based on a individual's condition and the usual course of the disease as seen in similar situations. Prognostic Factor,Prognostic Factors,Factor, Prognostic,Factors, Prognostic,Prognoses
D003661 Decision Support Techniques Mathematical or statistical procedures used as aids in making a decision. They are frequently used in medical decision-making. Decision Analysis,Decision Modeling,Models, Decision Support,Analysis, Decision,Decision Aids,Decision Support Technics,Aid, Decision,Aids, Decision,Analyses, Decision,Decision Aid,Decision Analyses,Decision Support Model,Decision Support Models,Decision Support Technic,Decision Support Technique,Model, Decision Support,Modeling, Decision,Technic, Decision Support,Technics, Decision Support,Technique, Decision Support,Techniques, Decision Support
D003663 Decision Trees A graphic device used in decision analysis, series of decision options are represented as branches (hierarchical). Decision Tree,Tree, Decision,Trees, Decision
D003922 Diabetes Mellitus, Type 1 A subtype of DIABETES MELLITUS that is characterized by INSULIN deficiency. It is manifested by the sudden onset of severe HYPERGLYCEMIA, rapid progression to DIABETIC KETOACIDOSIS, and DEATH unless treated with insulin. The disease may occur at any age, but is most common in childhood or adolescence. Diabetes Mellitus, Brittle,Diabetes Mellitus, Insulin-Dependent,Diabetes Mellitus, Juvenile-Onset,Diabetes Mellitus, Ketosis-Prone,Diabetes Mellitus, Sudden-Onset,Diabetes, Autoimmune,IDDM,Autoimmune Diabetes,Diabetes Mellitus, Insulin-Dependent, 1,Diabetes Mellitus, Type I,Insulin-Dependent Diabetes Mellitus 1,Juvenile-Onset Diabetes,Type 1 Diabetes,Type 1 Diabetes Mellitus,Brittle Diabetes Mellitus,Diabetes Mellitus, Insulin Dependent,Diabetes Mellitus, Juvenile Onset,Diabetes Mellitus, Ketosis Prone,Diabetes Mellitus, Sudden Onset,Diabetes, Juvenile-Onset,Diabetes, Type 1,Insulin Dependent Diabetes Mellitus 1,Insulin-Dependent Diabetes Mellitus,Juvenile Onset Diabetes,Juvenile-Onset Diabetes Mellitus,Ketosis-Prone Diabetes Mellitus,Sudden-Onset Diabetes Mellitus
D003924 Diabetes Mellitus, Type 2 A subclass of DIABETES MELLITUS that is not INSULIN-responsive or dependent (NIDDM). It is characterized initially by INSULIN RESISTANCE and HYPERINSULINEMIA; and eventually by GLUCOSE INTOLERANCE; HYPERGLYCEMIA; and overt diabetes. Type II diabetes mellitus is no longer considered a disease exclusively found in adults. Patients seldom develop KETOSIS but often exhibit OBESITY. Diabetes Mellitus, Adult-Onset,Diabetes Mellitus, Ketosis-Resistant,Diabetes Mellitus, Maturity-Onset,Diabetes Mellitus, Non-Insulin-Dependent,Diabetes Mellitus, Slow-Onset,Diabetes Mellitus, Stable,MODY,Maturity-Onset Diabetes Mellitus,NIDDM,Diabetes Mellitus, Non Insulin Dependent,Diabetes Mellitus, Noninsulin Dependent,Diabetes Mellitus, Noninsulin-Dependent,Diabetes Mellitus, Type II,Maturity-Onset Diabetes,Noninsulin-Dependent Diabetes Mellitus,Type 2 Diabetes,Type 2 Diabetes Mellitus,Adult-Onset Diabetes Mellitus,Diabetes Mellitus, Adult Onset,Diabetes Mellitus, Ketosis Resistant,Diabetes Mellitus, Maturity Onset,Diabetes Mellitus, Slow Onset,Diabetes, Maturity-Onset,Diabetes, Type 2,Ketosis-Resistant Diabetes Mellitus,Maturity Onset Diabetes,Maturity Onset Diabetes Mellitus,Non-Insulin-Dependent Diabetes Mellitus,Noninsulin Dependent Diabetes Mellitus,Slow-Onset Diabetes Mellitus,Stable Diabetes Mellitus
D003930 Diabetic Retinopathy Disease of the RETINA as a complication of DIABETES MELLITUS. It is characterized by the progressive microvascular complications, such as ANEURYSM, interretinal EDEMA, and intraocular PATHOLOGIC NEOVASCULARIZATION. Diabetic Retinopathies,Retinopathies, Diabetic,Retinopathy, Diabetic
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000066491 Clinical Decision-Making Process of formulating a diagnosis based on medical history and physical or mental examinations, and/or choosing an appropriate intervention. Medical Decision-Making,Clinical Decision Making,Decision-Making, Clinical,Decision-Making, Medical,Medical Decision Making
D000069550 Machine Learning A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data. Transfer Learning,Learning, Machine,Learning, Transfer

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