Subgroup Discovery: Real-World Applications

Subgroup discovery is a data mining technique which extracts interesting rules with respect to a target variable. An important characteristic of this task is the combination of predictive and descriptive induction. In this paper, an overview about subgroup discovery is performed. In addition, di erent real-world applications solved through evolutionary algorithms where the suitability and potential of this type of algorithms for the development of subgroup discovery algorithms are presented

Analysing the Moodle e-learning platform through subgroup discovery algorithms based on evolutionary fuzzy systems

Nowadays, there is a increasing in the use of learning management systems from the universities. This type of systems are also known under other di erent terms as course management systems or learning content management systems. Speci cally, these systems are e-learning platforms o ering di erent facilities for information sharing and communication between the participants in the e-learning process. This contribution presents an experimental study with several subgroup discovery algorithms based on evolutionary fuzzy systems using data from a web-based education system. The main objective of this contribution is to extract unusual subgroups to describe possible relationships between the use of the e-learning platform and marks obtained by the students. The results obtained by the best performing algorithm, NMEEF-SD, are also presented. The most representative results obtained by this algorithm are summarised in order to obtain knowledge that can allow teachers to take actions to improve student performance

Subgroup Discovery trhough Evolutionary Fuzzy Systems applied to Bioinformatic problems

Subgroup discovery is a descriptive data mining technique using supervised learning. This paper presents a summary about the main properties and elements about subgroup discovery task. In addition, we will focus on the suitability and potential of the search performed by evolutionary algorithms in order to apply in the development of subgroup discovery algorithms, and in the use of fuzzy logic which is a soft computing technique very close to the human reasoning. The hybridisation of both techniques are well known as evolutionary fuzzy system. The most relevant applications of evolutionary fuzzy systems for subgroup discovery in the bioinformatics domains are outlined in this work. Specifically, these algorithms are applied to a problem based on the Influenza A virus and the accute sore throat problem

Evaluación de pacientes con fractura de radio distal tratados con fijación percutánea

En fracturas del radio distal existe una tendencia hacia la reducción abierta y fijación interna, sin embargo, el tratamiento mediante reducción cerrada y enclavamiento percutáneo continúa ofreciendo buenos resultados pero es necesaria una terapia física y rehabilitación temprana y adecuada. Material y métodos: Evaluamos a pacientes con fractura de radio distal tratados mediante reducción por maniobras y enclavamiento percutáneo, utilizamos escalas clínicas funcionales como la escala de DASH, la escala de muñeca de la Clínica Mayo y Escala Visual Análoga para valorar dolor. Los resultados de los pacientes fueron comparados a las 12 y 24 semanas. Así mismo comparamos a pacientes con o sin rehabilitación. Resultados: se evaluó a 60 pacientes con la escala DASH, en pacientes con rehabilitación obtuvieron 4.3 puntos, mientras que sin rehabilitación fue de 10.5 puntos (p = 0.00001), en la escala de la Clínica Mayo con rehabilitación tuvieron 86.7 puntos y sin rehabilitación tuvieron 77.8 puntos (p = 0.00001). El EVA no fue significativo. Conclusión: Todos los pacientes mostraron mejoría en sus escalas de evaluación clínica. Sin embargo, al comparar a pacientes con rehabilitación y sin rehabilitación la diferencia fue mayor en la escala de la Clínica Mayo y en la escala de DAS

Sonic black holes in dilute Bose-Einstein condensates

The sonic analog of a gravitational black hole in dilute-gas Bose-Einstein condensates is investigated. It is shown that there exist both dynamically stable and unstable configurations which, in the hydrodynamic limit, exhibit behaviors completely analogous to that of gravitational black holes. The dynamical instabilities involve the creation of quasiparticle pairs in positive and negative energy states. We illustrate these features in two qualitatively different one-dimensional models, namely, a long, thin condensate with an outcoupler laser beam providing an "atom sink" and a tight ring-shaped condensate. We also simulate the creation of a stable sonic black hole by solving the Gross-Pitaevskii equation numerically for a condensate subject to a trapping potential which is adiabatically deformed. A sonic black hole could, in this way, be created experimentally with state-of-the-art or planned technology

Application of data augmentation techniques towards metabolomics

Niemann–Pick Class 1 (NPC1) disease is a rare and debilitating neurodegenerative lysosomal storage disease (LSD). Metabolomics datasets of NPC1 patients available to perform this type of analysis are often limited in the number of samples and severely unbalanced. In order to improve the predictive capability and identify new biomarkers in an NPC1 disease urinary dataset, data augmentation (DA) techniques based on computational intelligence have been employed to create synthetic samples, i.e. the addition of noise, oversampling techniques and conditional generative adversarial networks. These techniques have been used to evaluate their predictive capacities on a set of urine samples donated by 13 untreated NPC1 disease and 47 heterozygous (parental) carrier control participants. Results on the prediction have also been obtained using different machine learning classification models and the partial least squares techniques. These results provide strong evidence for the ability of DA techniques to generate good quality synthetic data. Results acquired show increases in sensitivity of 20%–50%, an F1 score of 6%–30%, and a predictive capacity of 0.3 (out of 1). Additionally, more conventional forms of multivariate data analysis have been employed. These have allowed the detection of unusual urinary metabolite profiles, and the identification of biomarkers through the use of synthetically augmented datasets. Results indicate that urinary branched-chain amino acids such as valine, 3-aminoisobutyrate and quinolinate, may be employable as valuable biomarkers for the diagnosis and prognostic monitoring of NPC1 diseaseThe authors acknowledge the support from MINECO (Spain) through grants TIN2017-88728-C2-1-R and PID2020-116898RB-I00 (MICINN), from Universidad de Málaga y Junta de Andalucía through grant UMA20-FEDERJA-045, and from Instituto de Investigación Biomédica de Málaga – IBIMA (all including FEDER funds). Funding for open access charge: Universidad de Málaga / CBUA