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Tuberculosis is a respiratory disease that affects lungs and it is caused by the bacillus Mycobacterium tuberculosis (MTB), which is spread when people who are sick with tuberculosis expel bacteria into the air by coughing. Before the coronavirus (COVID-19) pandemic, tuberculosis was the leading cause of death from infectious agents even ranking above HIV/AIDS. Growing MTB on solid medium is the main diagnostic reference method. In addition, this is the standard method for identifying the bacterial resistance profile. However, the waiting period for the results is long, during which time a patient can be highly contagious. In turn, delaying treatment for tuberculosis can increase disease severity, but treating without diagnostic confirmation can lead to bacterial resistance, a higher rate of adverse events, and higher costs. Therefore, pulmonary tuberculosis disease must be rapidly diagnosed by a costeffective method. This paper proposes models for the detection of pulmonary tuberculosis by using different artificial intelligence techniques. These models can be used to support decision making of doctors and are intended to identify whether a patient has tuberculosis or not. In particular, four supervised learning techniques are used (neural networks, decision trees, and two ensemble methods). Each model allows predicting a positive or negative diagnosis of pulmonary tuberculosis based on previously recorded diagnostic variables taken from patients in Cali, Colombia. According to the results, the Extra Trees method reaches the highest accuracy compared to the other techniques used for the prediction of pulmonary tuberculosis with an area under the ROC curve of 95,63%. [ABSTRACT FROM AUTHOR]

In this paper, new models for credit risk prediction in Colombia are proposed by using different artificial intelligence techniques. These models can be used to support the risk management area in banks, and they aim to identify clients that could be in default, generating a possible credit risk for financial institutions. Three techniques are used to obtain the models (neuronal networks, decision trees, and support vector machines) that predict the next payment of a client's fee based on basic data from the client and previous recorded installment payments. Decision trees turns out to be more accurate than the other techniques that have been used when predicting credit risk with a ROC area of 88.29%. The proposed models reach accuracies that are like some other papers in the state of the art and in some cases, they overcome models in other studies. [ABSTRACT FROM AUTHOR]

In this paper is shown the application of neural networks in order to predict academic marks that will be obtained for the students in the subjects of Data Structures I and II, included both in the Informatics Engineering curricula at Higher Polytechnic Institute José Antonio Echeverría in the Republic of Cuba. The main motivation for the present work is justified because selected subjects have a high level of complexity, demanding from the student to be rigorous and a permanent dedication. As a consequence the academic results obtained at the present time are not satisfactory. To reach the goal mentioned above a software based on MATLAB tool was developed and the marks obtained previously by students in some subjects and others data of interest were used. Two neural networks were employed, both with the same architecture, but each one trained with the specific data of each subject (Data Structures I and II). A group of experiments was carried out to contrast the behavior of the neural networks regarding some specific statistics in the data of the sample. An overall effectiveness in prediction superior to 78% for the case of the first subject was achieved, while for the second one effectiveness superior to 75% was reached. [ABSTRACT FROM AUTHOR]