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Objective: This study aimed to analyze risk factors for amputation (overall, minor and major) in patients with diabetic foot ulcers (DFUs). Methods: 407 patients with DFUs (286 male, 121 female; mean age = 60, age range = 32-92) who were managed in a tertiary care centre from 2009 to 2019 were retrospectively identified and included in the study. DFUs were categorized based on the Meggit-Wagner, PEDIS, S(AD)SAD, and University of Texas (UT) classification systems. To identify amputation risk-related factors, results of patients with DFUs who underwent amputations (minor or major) were compared to those who received other adjunctive treatments using Chi-Square, one-way analysis of variance (ANOVA) and Spearman correlation analysis. Results: The mean C-reactive protein (CRP) and White Blood Cell (WBC) values were significantly higher in patients with major or minor amputation than in those without amputation. The mean Neutrophil (PNL), Platelets (PLT), wound width, creatinine and sedimentation (ESR) values were significantly higher in patients with major amputation compared to other groups of patients. Elevated levels of High-density lipoprotein (HDL), Hemoglobin (HGB) and albumin were determined to be protective factors against the risk of amputation. Spearman correlation analysis revealed a positive-sided, strong-levelled, significant relation between Wagner grades and amputation status of patients. Conclusion: This study has identified specific factors for major and minor amputation risk of patients with DFUs. Especially infection markers such as CRP, WBC, ESR and PNL were higher in the amputation group. Most importantly, Meggit Wagner, one of the four different classification systems used in the DFUs, was determined to be highly associated with patients’ amputation risk. Level of Evidence: Level IV, Prognostic Study

Diabetes mellitus is one of the most important public health problems affecting millions of people worldwide. An early and accurate diagnosis of diabetes mellitus has critical importance for the medical treatments of patients. In this study, first, artificial neural network (ANN) and classification and regression tree (CART)-based approaches are proposed for the diagnosis of diabetes.Hybrid ANN-GA and CART-GA approaches are then developed using a genetic algorithm (GA) to improve the classification accuracy of these approaches. Finally, the performances of the developed approaches are evaluated with a Pima Indian diabetes data set. Experimental results show that the developed hybrid CART-GA approach outperforms the ANN, CART, and ANN-GA approaches in terms of classification accuracy, and this approach provides an efficient methodology for diagnosis of diabetes mellitus.