Approach for predicting dropout in a health club

Este estudo pretende apresentar um modelo para prever o abandono dos clientes num ginásio, baseado em dados existentes no software de gestão Health Center. As variáveis selecionadas, identificadas de acordo com a sua relevância em estudos realizados e disponibilidade de dados, foram: idade, género, tempo de inscrição, média de visitas mensais, faturação realizada ao cliente, número de aulas frequentadas e distância a percorrer para chegar ao clube. O número de clientes utilizados para a previsão de abandono foram de 810, através da utilização de um algoritmo de Machine Learning Two-class logistic regression para a classificação. A aproximação realizada permitiu prever com uma exatidão de 83% se o cliente abandonava ou ficava no ginásio. Os resultados obtidos sugerem que pode ser vantajoso a utilização da aproximação realizada para prever o abandono e explorar medidas adicionais para contrariar o abandono de clientes em risco.Este estudio pretende presentar un modelo para prever el abandono de los clientes en un gimnasio, basado en datos existentes en el software de gestión Health Center. Las variables seleccionadas, identificadas de acuerdo con la su relevancia identificada en estudios realizados y disponibilidad de datos, fueron: edad, género, tiempo de inscripción, promedio de visitas mensuales, facturación realizada al cliente, número de clases frecuentadas y distancia a recorrer para llegar al club. El número de clientes utilizados para la previsión de abandono fue de 810, mediante el uso de un algoritmo de Machine Learning Two-class logistic regression para la clasificación. La aproximación realizada permitió prever con una exactitud del 83% si el cliente abandonaba o quedaba lo gimnasio. Los resultados obtenidos sugieren que puede ser ventajoso el uso de la aproximación realizada para prever el abandono y explotar medidas adicionales para contrarrestar el abandono de clientes en riesgo.This study aimed to develop a model to predict customers dropouts in a health club, using existing data in the software Health Center used to manage the health club. The variables selected, identified according to the relevance in studies performed and availability of data, were: age, gender, enrollment time, average monthly visits, customer billing, number of classes attended and distance to reach the club. The number of customers used to develop the dropout prediction were 810, using a Machine Learning algorithm Two-class logistic regression for the classification. The approach adopted allowed to predict with an accuracy of 83% if the client left or stayed in the gym. The results suggest that can be useful tool to predict dropout and to use additional approaches to counteract clients in risk to dropout.info:eu-repo/semantics/publishedVersio

A SLR on Customer Dropout Prediction

Dropout prediction is a problem that is being addressed with machine learning algorithms; thus, appropriate approaches to address the dropout rate are needed. The selection of an algorithm to predict the dropout rate is only one problem to be addressed. Other aspects should also be considered, such as which features should be selected and how to measure accuracy while considering whether the features are appropriate according to the business context in which they are employed. To solve these questions, the goal of this paper is to develop a systematic literature review to evaluate the development of existing studies and to predict the dropout rate in contractual settings using machine learning to identify current trends and research opportunities. The results of this study identify trends in the use of machine learning algorithms in different business areas and in the adoption of machine learning algorithms, including which metrics are being adopted and what features are being applied. Finally, some research opportunities and gaps that could be explored in future research are presented.info:eu-repo/semantics/publishedVersio

ANÁLISE DE CLUSTERS PARA SEGMENTAÇÃO DE ESTUDANTES NUMA INSTITUIÇÃO DE ENSINO SUPERIOR

A segmentação do mercado é um tema importante para os administradores das instituições de ensino superior. A segmentação dos alunos permite a diferenciação e a definição de ações personalizadas de acordo com cada segmento e pode ser realizada recorrendo a dados existentes de alunos para serem posteriormente utilizados no desenvolvimento de ações de comunicação ou para realização de um acompanhamento interno diferenciado. A metodologia utilizada para realizarmos a segmentação dos alunos (n=280) recorreu à análise de clusters utilizando o algoritmo k-means disponível na biblioteca scikit. O k-means é um algoritmo não supervisionado para a determinação dos clusters, que requer que o investigador determine à priori o número de clusters pretendidos, utilizando uma aproximação iterativa calculando o centro ótimo de cada cluster. A identificação do número de clusters foi baseada no método elbow, que utiliza o pressuposto de que o número de clusters ótimo é aquele em que adicionando mais clusters não reduz significativamente a variância entre clusters. Depois de obtivermos cada cluster realizamos a sua caraterização utilizando as variáveis existentes para termos uma melhor compreensão dos dados. Os resultados obtidos permitiram identificar três clusters, onde obtivemos no cluster um 89 alunos, cluster dois 16 alunos e cluster três 175 alunos. Para facilitar a compreensão dos resultados obtidos realizamos a redução das variáveis existentes através de do Principal Components Analysis, uma redução de dimensões para podemos projetar os dados num espaço dimensional menor de duas dimensões, num gráfico de dispersão x,y. Realizamos a caraterização (média±desvio padrão) das variáveis idade, ano, estado civil e sexo. Os resultados obtidos evidenciam que nos clusters um, dois e três as médias de idades são aproximadamente iguais 28,29 e 31, o estado civil é maioritariamente solteiros com 80%, 81% e 75% e o sexo feminino representa 49%, 51% e 50% respetivamente. Os resultados conseguidos não são elucidativos considerando os indicadores obtidos em cada cluster. Para podermos retirar melhores conclusões deveriam ser incluídas mais variáveis, como cursos frequentados, resultados obtidos na frequência do curso e aumentar a amostra. Um aspeto que poderia ter sido equacionado seria a normalização dos dados, reduzindo impacto de variáveis em escalas diferentes na determinação do número de clusters. Por último seria interessante explorar as diferenças entre os alunos nos clusters existentes realizando a análise das variáveis existentes.info:eu-repo/semantics/publishedVersio

Extraction of biomedical indicators from gait videos

Gait has been an extensively investigated topic in recent years. Through the analysis of gait it is possible to detect pathologies, which makes this analysis very important to assess anomalies and, consequently, help in the diagnosis and rehabilitation of patients. There are some systems for analyzing gait, but they are usually either systems with subjective evaluations or systems used in specialized laboratories with complex equipment, which makes them very expensive and inaccessible. However, there has been a significant effort of making available simpler and more accurate systems for gait analysis and classification. This dissertation reviews recent gait analysis and classification systems, presents a new database with videos of 21 subjects, simulating 4 different pathologies as well as normal gait, and also presents a web application that allows the user to remotely access an automatic classification system and thus obtain the expected classification and heatmaps for the given input. The classification system is based on the use of gait representation images such as the Gait Energy Image (GEI) and the Skeleton Gait Energy Image (SEI), which are used as input to a VGG-19 Convolutional Neural Network (CNN) that is used to perform classification. This classification system is a vision-based system. To sum up, the developed web application aims to show the usefulness of the classification system, making it possible for anyone to access it.A marcha tem sido um tema muito investigado nos últimos anos. Através da análise da marcha é possível detetar patologias, o que torna esta análise muito importante para avaliar anómalias e consequentemente, ajudar no diagnóstico e na reabilitação dos pacientes. Existem alguns sistemas para analisar a marcha, mas habitualmente, ou estão sujeitos a uma interpretação subjetiva, ou são sistemas usados em laboratórios especializados com equipamento complexo, o que os torna muito dispendiosos e inacessíveis. No entanto, tem havido um esforço significativo com o objectivo de disponibilizar sistemas mais simples e mais precisos para análise e classificação da marcha. Esta dissertação revê os sistemas de análise e classificação da marcha desenvolvidos recentemente, apresenta uma nova base de dados com vídeos de 21 sujeitos, a simular 4 patologias diferentes bem como marcha normal, e apresenta também uma aplicação web que permite ao utilizador aceder remotamente a um sistema automático de classificação e assim, obter a classificação prevista e mapas de características respectivos de acordo com a entrada dada. O sistema de classificação baseia-se no uso de imagens de representação da marcha como a "Gait Energy Image" (GEI) e "Skeleton Gait Energy Image" (SEI), que são usadas como entrada numa rede neuronal convolucional VGG-19 que é usada para realizar a classificação. Este sistema de classificação corresponde a um sistema baseado na visão. Em suma, a aplicação web desenvolvida tem como finalidade mostrar a utilidade do sistema de classificação, tornando possível o acesso a qualquer pessoa

Hybrid Random Forest Survival Model to Predict Customer Membership Dropout

Dropout prediction is a problem that must be addressed in various organizations, as retaining customers is generally more profitable than attracting them. Existing approaches address the problem considering a dependent variable representing dropout or non-dropout, without considering the dynamic perspetive that the dropout risk changes over time. To solve this problem, we explore the use of random survival forests combined with clusters, in order to evaluate whether the prediction performance improves. The model performance was determined using the concordance probability, Brier Score and the error in the prediction considering 5200 customers of a Health Club. Our results show that the prediction performance in the survival models increased substantially in the models using clusters rather than that without clusters, with a statistically significant difference between the models. The model using a hybrid approach improved the accuracy of the survival model, providing support to develop countermeasures considering the period in which dropout is likely to occur.info:eu-repo/semantics/publishedVersio

Integrating Knowledge Management in a Business Strategy Process Operationalized using Process Management Approach

This paper proposes an integrated approach applied in a business environment, using three organisational layers materialized in the institutional, middle and operational levels. Is suggested a combination of Business Strategy, Knowledge Management and Business Process Management in order to support the clarification of the organisational strategy and the definition of business operations. The work in progress developed uses a theoretical framework based in emergent theories of strategy management combining two types of strategy intended or deliberate and emergent, clarifying important constructs like mission, vision, strategic objectives, stakeholders, business capabilities and knowledge objects, interpreted as a business context that facilitates the following steps of analysis and provides priorities of improvement. The priorities identify targets to improved using Business Process Management (BPM) approaches combined with the knowledge concepts in BPM models. The proposed approach was applied in a public organisation that develops its activities in the areas of Olympic preparation, swimming performance and sport facilities. The outcomes of the work develop, were the systematization of the business processes related to the structured work and the use of knowledge management concepts in the exception handling of the processes. The representation of the unstructured work or the modelling of complex processes was combined with the use knowledge constructs, properly contextualized in business strategy axioms. The research findings identify advantages in the use of knowledge concepts in complex process model, exception handling and in classifying the knowledge used in decisions. This could facilitate the definition of training actions articulated with the organisation real needs.N/

Sports Participation and Value of Elite Sports in Predicting Well-Being

This work contributes to emerging literature focused on the role of physical activity on the subjective well-being of populations. Unlike the existing literature, it proposes an approach that uses algorithms to predict subjective well-being. The aims of this study were to determine the relative importance of sports participation and the perceived value of elite sports on the subjective well-being of individuals. A total of 511 participants completed an online questionnaire. The statistical analysis used several machine learning techniques, including three algorithms, Decision Tree Classifier (DTC), Random Forest Classifier (RFC), and Gradient Boosting Classifier (GBC). In the three algorithms tested, sports participation, expressed as the weekly frequency and the time spent engaging in vigorous physical activity, showed a greater importance (between 47% and 53%) in determining subjective well-being. It also highlights the effect of perceived value of elite sport on the prediction of subjective well-being. This study provides evidence for public sport policy makers/authorities and for managers of physical activity and sport development programs. The surprising effect of the perceived value of elite sport on the prediction of subjective well-being.info:eu-repo/semantics/publishedVersio

BUSINESS INTELLIGENCE IN THE TOOL MANAGEMENT USED BY THE CUT AND CNC MACHINES OF THE ORNAMENTAL ROCKS INDUSTRY

In the ornamental stone industry, improving production processes is a constant challenge for managers looking for solutions that improve the competitiveness of the companies. The management of cutting and computerized cut command machine tools is one of the areas where this improvement in management processes can have a positive effect on the competitiveness and productivity of companies. With this purpose, a model based on business intelligence methodologies was developed to systematize and automate the management process. The proposed model consists of the following layers: acquisition, extract, transform and load, storage and access and analysis. The acquisition layer consists of the interface with data available in various formats. The extract, transform and load process aims to extract data from these repositories and load them into a data warehouse. While the access and analysis phase is based on the use of software tools with graphical user interface with advanced analysis reporting features. The technology infrastructure is supported by the open source Tibco Jaspersoft Community Edition software package, which provides tools for the practical implementation of the defined model. With this work, it is hoped to implement the defined business intelligence model thus giving answers to the problems identified by providing information for the decision-making that corresponds to the needs of the managers.info:eu-repo/semantics/publishedVersio

Strategy operationalization in a Taekwondo Federation

[EN] The management of sport requires addressing the organization structures and systems, considering simultaneously..

ICT and management of coronary artery disease from the perspective of health professionals

Cardiovascular diseases remain the leading cause of death in the world, however, in the case of coronary artery disease, most of these deaths are preventable through lifestyle modification, reduction of risk factors and involvement of each patient in the surveillance of his condition. The current dynamism on the health technology market makes access to information and communication technologies (ICTs), increasingly accessible to the population, including wearable devices capable of evaluating vital signs. Understanding the perspectives of healthcare professionals in the use of these technologies in clinical settings for surveillance and promotion of the health status of patients with coronary heart disease may help to bring technological advances in health ICTs closer to the expectations of clinical practice. After two sessions of focus groups, we present the results obtained from four professional groups: Nurses, Cardiac Physiologists, Physiotherapists and Physicians