Abstract
One of the main microvascular complications presented in the Mexican population is diabetic retinopathy which affects 27.50% of individuals with type 2 diabetes. Therefore, the purpose of this study is to construct a predictive model to find out the risk factors of this complication. The dataset contained a total of 298 subjects, including clinical and paraclinical features. An analysis was constructed using machine learning techniques including Boruta as a feature selection method, and random forest as classification algorithm. The model was evaluated through a statistical test based on sensitivity, specificity, area under the curve (AUC), and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model obtaining 69% of AUC. Moreover, a risk evaluation was incorporated to evaluate the impact of the predictors. The proposed method identifies creatinine, lipid treatment, glomerular filtration rate, waist hip ratio, total cholesterol, and high density lipoprotein as risk factors in Mexican subjects. The odds ratio increases by 3.5916 times for control patients which have high levels of cholesterol. It is possible to conclude that this proposed methodology is a preliminary computer-aided diagnosis tool for clinical decision-helping to identify the diagnosis of DR.
Highlights
IntroductionDistinguished as a global health emergency, it affects more than 463 million people worldwide; and it is expected to exceed 578 million by 2030 and 700 by 2045, becoming the seventh leading cause of death in 2030 [2]
Diabetes Mellitus (DM) is a metabolic disorder characterized by hyperglycemia, resulting from the inability of the pancreas to produce enough insulin [1].Distinguished as a global health emergency, it affects more than 463 million people worldwide; and it is expected to exceed 578 million by 2030 and 700 by 2045, becoming the seventh leading cause of death in 2030 [2]
Sensitivity provides the portion of positive instances that were correctly classified, and the specificity, the portion of negative instances that were correctly classified
Summary
Distinguished as a global health emergency, it affects more than 463 million people worldwide; and it is expected to exceed 578 million by 2030 and 700 by 2045, becoming the seventh leading cause of death in 2030 [2]. Another alarming aspect is the high percentage of people with undiagnosed DM, which currently exceeds 50%, being in most cases type 2 diabetes (T2D) [2]. T2D represents 90–95% of DM cases worldwide; results from a pancreatic β cell dysfunction combined by insulin resistance. It is important to point out that T2D is prevalent in Latin Americans due to a combination of genetic and lifestyle risk factors [3]
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