Introduction Diabetes mellitus (DM) is a main, highly prevalent, and challenging public health issue. Suboptimal self-care for type II diabetes can lead to poor glycemic control, complications, and even death. Objective This study investigated the incidence of distress and its link with self-care habits of patients with diabetes type II. Methods A correlational, cross-sectional design with a convenient sample of 200 patients was used to conduct this study. Three questionnaires were administered: (A) the demographic and medical data questionnaire; (B) diabetes self-care activities in brief; and (C) the diabetes distress scale in Arabic language. Results The patients’ mean age was 51.78 ± 11.34; 80% of patients practiced lower levels of diabetes self-care, and 37% of them had a high level of diabetes distress. Self-care is associated with diabetes distress ( R = −0.152, p-value = .032). Conclusion Self-care activities can help in the early detection and management of diabetes distress. Sustained self-care education is promising to minimize diabetes distress. The potential advantages of association between diabetes distress and self-care can offer self-care programs that enhance diabetes distress management.

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Introduction In Egypt, diabetic foot ulcers markedly contribute to the morbidity and mortality of diabetic patients. Accurately predicting the risk of diabetic foot ulcers could dramatically reduce the enormous burden of amputation. Objective The aim of this study is to design an artificial intelligence-based artificial neural network and decision tree algorithms for the prediction of diabetic foot ulcers. Methods A case–control study design was utilized to fulfill the aim of this study. The study was conducted at the National Institute of Diabetes and Endocrine Glands, Cairo University Hospital, Egypt. A purposive sample of 200 patients was included. The tool developed and used by the researchers was a structured interview questionnaire including three parts: Part I: demographic characteristics; Part II: medical data; and Part III: in vivo measurements. Artificial intelligence methods were used to achieve the aim of this study. Results The researchers used 19 significant attributes based on medical history and foot images that affect diabetic foot ulcers and then proposed two classifiers to predict the foot ulcer: a feedforward neural network and a decision tree. Finally, the researchers compared the results between the two classifiers, and the experimental results showed that the proposed artificial neural network outperformed a decision tree, achieving an accuracy of 97% in the automated prediction of diabetic foot ulcers. Conclusion Artificial intelligence methods can be used to predict diabetic foot ulcers with high accuracy. The proposed technique utilizes two methods to predict the foot ulcer; after evaluating the two methods, the artificial neural network showed a higher improvement in performance than the decision tree algorithm. It is recommended that diabetic outpatient clinics develop health education and follow-up programs to prevent complications from diabetes.