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.

Background: Diabetes mellitus is a global health challenge, especially among homeless people. Early prediction of diabetes can reduce treatment costs and improve interventions. This study aimed to identify predictors of diabetes among homeless adults by utilizing artificial intelligence and providing recommendations for diabetes prevention. Methods: A case-control study of 150 homeless adults in Giza, Egypt (99 diabetes cases and 51 controls), analyzed 43 variables collected through interviews and physiological measures, with missing data imputed. Feature selection using recursive feature elimination and univariate and correlation analyses reduced the predictors to 13 variables. The class imbalance was addressed using synthetic minority over-sampling on the training set. Six models and a stacking ensemble with XGBoost as a meta-learner were evaluated using 5-fold cross-validation and performance metrics, including the accuracy, precision, recall, F1-score, and AUC-ROC. Results: The key predictors included BMI, systolic blood pressure, triceps skinfold thickness, waist circumference, lifestyle factors, comorbidities, diastolic blood pressure, age, medication adherence, educational level, marital status, duration of residence, and diabetes knowledge. Individual classifiers achieved a moderate performance (accuracy: 56.7–70.0%, F1-score: 0.686–0.781). The stacking ensemble substantially outperformed individual models, achieving a 95.45% accuracy, a 100% precision, a 93.75% recall, a 0.968 F1-score, and a 0.979 AUC-ROC on the test set. Conclusions: Machine learning models can reliably predict diabetes. The proposed hybrid stacking model outperformed conventional classifiers in terms of the prediction performance, highlighting the benefits of ensemble learning and sophisticated resampling strategies in dealing with imbalanced medical data. It is recommended that healthcare institutions integrate AI-powered diagnostic assistance technology into clinical processes to aid in the early detection and treatment of diabetes.

Background: Intestinal parasitic infection is widespread worldwide and a serious public health problem. It is responsible for enormous morbidity and mortality around the world, particularly in developing countries. Artificial intelligence can create therapeutic suggestions that are individually tailored to each client’s unique traits. This study aimed to develop an intelligent system for mothers to provide future recommendations for more effective interventions to combat intestinal parasitic infections. Methods: A quasi-experimental research design (pre/post-test) was utilized to achieve the aim of the current study. The study was conducted at Dar Al Salam Family Health Center in Cairo Governorate, Egypt. A purposive sample of 200 mothers was included in this study. Two tools were used for data collection: First tool: Structured knowledge questionnaire that has four parts: Part I: Demographic data; Part II: Family history for intestinal parasitic infection; Part III: Home environment; Part IV: Mothers’ knowledge regarding intestinal parasitic infection. Second tool: Mothers’ reported preventive measures checklist. Results: There was a highly significant statistical difference in the mothers’ total knowledge level and total preventive measures regarding intestinal parasitic infection between pre and posttest. Moreover, there was highly significant statistical positive correlation between mothers’ total knowledge and total preventive measures at pre and post-test. The integration of large language model driven insights, quick engineering, and tailored treatments demonstrated favorable outcomes in enhancing mothers’ cleanliness routines. The novelty of this study lies in integrating K-Means clustering, large language models (GPT-4o), and prompt engineering to generate culturally tailored and behavior-specific preventive recommendations for mothers regarding intestinal parasitic infections. Conclusions: Preventive educational programs have a significant positive effect on improving mothers’ knowledge and preventive measures to combat intestinal parasitic infections. This study effectively underscores the significance of artificial intelligence-driven analysis and large language models in producing tailored suggestions to enhance mothers’ hygiene routines.