Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising.

The use of augmented reality (AR) with GeoGebra allows for the contextualization of mathematical operations in real-world situations. In this approach, the teacher presents questions or problems that students solve using visualization and experimentation software. The objective of this work is to evaluate the impact of integrating the GeoGebra 3D calculator with AR. For the development of this study, the quasi-experimental method was employed, involving the comparison of results between two groups: the experimental group (EG) and the control group (CG). We worked with a population of 78 students. The study conducted confirms the use of the GeoGebra calculator in 3D with AR. AR effectively enhances mathematical learning. Seventy percent of the students in the EG achieved an outstanding level of performance, while 30% reached an expected level. In addition, a positive attitude towards mathematics was observed in 100% of the students. These results demonstrate that using the GeoGebra calculator in 3D with AR has a positive impact on mathematics learning. While in CG, 10% achieved the expected level of performance, 85% were in progress, and 5% were at the initial stage. Finally, it was concluded that the GeoGebra calculator in 3D with AR is very useful. It helps enhance the teaching and learning (TL) of mathematics and motivates students, making the development of class sessions more dynamic.

The systematic review aimed to investigate the influence of augmented reality on the learning and teaching process of geometry between 2017 and 2024. For this purpose, five main databases were used: Scopus, Science Direct, Springer, IOPScience and EBSCO, as well as additional databases. The PRISMA methodology was used in the search and information gathering process, executing its four phases and applying the eligibility criteria of the papers. In total, 44 articles, 4 conferences and 2 book sections were collected. The years 2020, 2022 and 2023 stood out for having the highest number of articles, with 9 each, representing 54% of the total. The United Kingdom and Turkey were the countries with the highest scientific production, contributing 20% of the articles. In addition, the predominant methodology was quantitative, with a total of 29 studies. Finally, the systematic review concludes that augmented reality offers great opportunities for education, providing a significant advantage in the geometry teaching process compared to traditional methods.