As a global health problem, cervical cancer generates much information that circulates through social networks. Modeling allows us to automatically identify the topics that deal with a specific subject matter in a set of documents. This research used the LDA algorithm and the coherence metric for topic modeling and identified seven topics in a set of tweets on cervical cancer. The topics were related to the effect of HPV vaccines, the relationship between HPV and other diseases, forms of prevention such as vaccines and Papanicolaou tests, programs that provide medical services for the prevention and elimination of this disease, stories of women who have had cervical cancer and studies aimed at Latina women.

Urinary tract infections are the main reason for consultation in the pediatric emergency department worldwide, so it deserves to be analyzed with artificial intelligence techniques to discover patterns based on medical and laboratory information. Cluster analysis is an unsupervised machine learning technique that allows the identification of groups of patients with similar characteristics. In this work we analyzed information from patients whose anonymized information was extracted from a computer system, all of them are patients suffering from urinary tract infections. Multiple Correspondence Analysis was initially applied and then K-means and DBSCAN algorithms were used separately. The silhouette value of each group identified with the two algorithms was obtained. Patients were differentiated according to the prevalence percentages of sensitivity/resistance to certain antibiotics and t...

As a global health problem, cervical cancer generates much information that circulates through social networks. Modeling allows us to automatically identify the topics that deal with a specific subject matter in a set of documents. This research used the LDA algorithm and the coherence metric for topic modeling and identified seven topics in a set of tweets on cervical cancer. The topics were related to the effect of HPV vaccines, the relationship between HPV and other diseases, forms of prevention such as vaccines and Papanicolaou tests, programs that provide medical services for the prevention and elimination of this disease, stories of women who have had cervical cancer and studies aimed at Latina women.

Urinary tract infections are the main reason for consultation in the pediatric emergency department worldwide, so it deserves to be analyzed with artificial intelligence techniques to discover patterns based on medical and laboratory information. Cluster analysis is an unsupervised machine learning technique that allows the identification of groups of patients with similar characteristics. In this work we analyzed information from patients whose anonymized information was extracted from a computer system, all of them are patients suffering from urinary tract infections. Multiple Correspondence Analysis was initially applied and then K-means and DBSCAN algorithms were used separately. The silhouette value of each group identified with the two algorithms was obtained. Patients were differentiated according to the prevalence percentages of sensitivity/resistance to certain antibiotics and t...

This paper analyzes the impact of the variables phone addiction, pornography addiction, number of times the phone is unlocked per hour, and level of confidence in ChatGPT on the academic success of a group of 4278 students from eight universities in Ecuador. The decision trees (DT), random forest (RF), and support vector machine (SVM) methods are used. The results obtained indicate similar levels of precision achieved in the three algorithms; in terms of accuracy, in the case of SMOTE, DT is the algorithm that presents the highest accuracy (accuracy = 0,64); and, in the case of RandomOverSampler, the SVM algorithm had the highest accuracy (accuracy = 0,59).

This paper analyzes the impact of the variables phone addiction, pornography addiction, number of times the phone is unlocked per hour, and level of confidence in ChatGPT on the academic success of a group of 4278 students from eight universities in Ecuador. The decision trees (DT), random forest (RF), and support vector machine (SVM) methods are used. The results obtained indicate similar levels of precision achieved in the three algorithms; in terms of accuracy, in the case of SMOTE, DT is the algorithm that presents the highest accuracy (accuracy = 0,64); and, in the case of RandomOverSampler, the SVM algorithm had the highest accuracy (accuracy = 0,59).