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\n  \n 2023\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Analysis of Oversampling Techniques and Machine Learning Models on Unbalanced Spirometry Data.\n \n \n \n \n\n\n \n Izurieta, R., C.; Sánchez-Pozo, N., N.; Mejía-Ordóñez, J., S.; González-Vergara, J.; Sierra, L., M.; and Peluffo-Ordóñez, D., H.\n\n\n \n\n\n\n In Rocha, Á.; Ferrás, C.; and Ibarra, W., editor(s), Information Technology and Systems, pages 497-506, 2023. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n \n \"AnalysisWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Analysis of Oversampling Techniques and Machine Learning Models on Unbalanced Spirometry Data},\n type = {inproceedings},\n year = {2023},\n pages = {497-506},\n websites = {https://rd.springer.com/chapter/10.1007/978-3-031-33261-6_42},\n publisher = {Springer International Publishing},\n city = {Cham},\n id = {f1cec076-fc1e-3ce9-a354-1fa36ce613ff},\n created = {2025-08-09T03:22:37.512Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {4cc8259e-604d-3039-a35b-2896a39265ca},\n last_modified = {2025-08-09T03:22:37.512Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {10.1007/978-3-031-33261-6_42},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {The use of artificial intelligence in the quest to contribute to human longevity is becoming increasingly common in medical settings, one of these being spirometry. Given the different factors that can deteriorate the pulmonary status, several works aim to establish ways to alert future patients of their potential pulmonary complications. Thus, we carry out a lung age prediction task from spirometry data extracted using a previously developed mobile application. Regarding the imbalanced classes, SMOTE, ADASYN, and Random Oversampling algorithms were compared with different classifier models. The SMOTE and Quadratic Discriminant Analysis combination achieves a 99.12\\% accuracy, 99.09\\% specificity, and 99.91\\% sensitivity. Additionally, we performed an exploratory analysis of deep learning models, demonstrating that multilayer perceptron models, along with feature fusion techniques, achieve higher performances than classical models such as K-Nearest Neighbors or Decision Trees.},\n bibtype = {inproceedings},\n author = {Izurieta, Roberto Castro and Sánchez-Pozo, Nadia N and Mejía-Ordóñez, Juan S and González-Vergara, Juan and Sierra, Luz Marina and Peluffo-Ordóñez, Diego H},\n editor = {Rocha, Álvaro and Ferrás, Carlos and Ibarra, Waldo},\n booktitle = {Information Technology and Systems}\n}
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\n The use of artificial intelligence in the quest to contribute to human longevity is becoming increasingly common in medical settings, one of these being spirometry. Given the different factors that can deteriorate the pulmonary status, several works aim to establish ways to alert future patients of their potential pulmonary complications. Thus, we carry out a lung age prediction task from spirometry data extracted using a previously developed mobile application. Regarding the imbalanced classes, SMOTE, ADASYN, and Random Oversampling algorithms were compared with different classifier models. The SMOTE and Quadratic Discriminant Analysis combination achieves a 99.12\\% accuracy, 99.09\\% specificity, and 99.91\\% sensitivity. Additionally, we performed an exploratory analysis of deep learning models, demonstrating that multilayer perceptron models, along with feature fusion techniques, achieve higher performances than classical models such as K-Nearest Neighbors or Decision Trees.\n
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\n  \n 2021\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Predicting High School Students' Academic Performance: A Comparative Study of Supervised Machine Learning Techniques.\n \n \n \n \n\n\n \n Sánchez-Pozo, N., N.; Mejía-Ordóñez, J., S.; Chamorro, D., C.; Mayorca-Torres, D.; and Peluffo-Ordóñez, D., H.\n\n\n \n\n\n\n In 2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop, pages 1-6, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"PredictingWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Predicting High School Students' Academic Performance: A Comparative Study of Supervised Machine Learning Techniques},\n type = {inproceedings},\n year = {2021},\n pages = {1-6},\n websites = {https://ieeexplore.ieee.org/document/9733756},\n id = {f173cf7c-f24d-32ba-9eb7-8f4ca69a404c},\n created = {2025-08-09T03:22:37.501Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {4cc8259e-604d-3039-a35b-2896a39265ca},\n last_modified = {2025-08-09T03:22:37.501Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {9733756},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {The proliferation of mobile devices and the rapid development of information and communication technologies have revolutionized education. Educational data has evolved to be voluminously massive, broadly various, and produced at high velocity. Therefore, computerized techniques for integrating, processing, and transforming data into valuable knowledge have become necessary to improve internal academic processes. Specifically, educational data mining is an emerging discipline concerned with analyzing the massive amounts of academic data generated and stored by educational institutions. In this sense, machine learning algorithms aid decision-makers who are establishing strategies to improve students' learning experience and institutional effectiveness by revealing hidden patterns in academic performance. Thus, this paper describes our comparative study of machine learning techniques to predict academic performance. We selected the features that best fit the discovery of patterns in the academic performance of high school students, resulting in a balance between accuracy and interpretability. We implemented six supervised learning algorithms for pattern recognition: Light Gradient Boosting Machine, Gradient Boosting, AdaBoost, Logistic Regression, Random Forest, and K-nearest Neighbors. The experimental results showed that the Gradient Boosting (Gbc) algorithm achieved the highest accuracy (96.77%), superior to other classification techniques considered.},\n bibtype = {inproceedings},\n author = {Sánchez-Pozo, Nadia N and Mejía-Ordóñez, Juan S and Chamorro, Diana C and Mayorca-Torres, Dagoberto and Peluffo-Ordóñez, Diego H},\n doi = {10.1109/IEEECONF53024.2021.9733756},\n booktitle = {2021 Machine Learning-Driven Digital Technologies for Educational Innovation Workshop}\n}
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\n The proliferation of mobile devices and the rapid development of information and communication technologies have revolutionized education. Educational data has evolved to be voluminously massive, broadly various, and produced at high velocity. Therefore, computerized techniques for integrating, processing, and transforming data into valuable knowledge have become necessary to improve internal academic processes. Specifically, educational data mining is an emerging discipline concerned with analyzing the massive amounts of academic data generated and stored by educational institutions. In this sense, machine learning algorithms aid decision-makers who are establishing strategies to improve students' learning experience and institutional effectiveness by revealing hidden patterns in academic performance. Thus, this paper describes our comparative study of machine learning techniques to predict academic performance. We selected the features that best fit the discovery of patterns in the academic performance of high school students, resulting in a balance between accuracy and interpretability. We implemented six supervised learning algorithms for pattern recognition: Light Gradient Boosting Machine, Gradient Boosting, AdaBoost, Logistic Regression, Random Forest, and K-nearest Neighbors. The experimental results showed that the Gradient Boosting (Gbc) algorithm achieved the highest accuracy (96.77%), superior to other classification techniques considered.\n
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