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 ...

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 ...

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 math...