The high incidence and prevalence of chronic kidney disease (CKD), often caused by late diagnoses, is a critical public health problem, especially in developing countries such as Brazil. CKD treatment therapies, such as dialysis and kidney transplantation, increase the morbidity and mortality rates, besides the public health costs. This study analyses the usage of machine learning techniques to assist in the early diagnosis of CKD in developing countries. Qualitative and quantitative comparative analyses are, respectively, conducted using a systematic literature review and an experiment with machine learning techniques, with the k-fold cross-validation method based on the Weka software and a CKD dataset. These analyses enable a discussion on the suitability of machine learning techniques for screening for CKD risk, focusing on low-income and hard-to-reach settings of developing countries, due to the specific problems faced by them, e.g., inadequate primary health care. The study results show that the J48 decision tree is a suitable machine learning technique for such screening in developing countries, due to the easy interpretation of its classification results, with 95.00% accuracy, reaching a nearly perfect agreement with an experienced nephrologist's opinion. Conversely, random forest, naive Bayes, support vector machine, multilayer perceptron, and k-nearest neighbor techniques, respectively, yield 93.33%, 88.33%, 76.66%, 75.00%, and 71.67% accuracy, presenting at least moderate agreement with the nephrologist, at the cost of a more difficult interpretation of the classification results. INDEX TERMS Reviews, machine learning, medical diagnosis.
Abstract. Learning Management Systems (LMS) have contributed to the growth and popularity of distance learning modality. These LMSs are endowed with a variety of tools aimed at facilitating the process of teaching and learning. However, few studies have been conducted to evaluate the effectiveness of these tools. The objective of this study is to evaluate the use of these tools and their impact on the performance of students in the disciplines. This review was conducted in the LMS Moodle and the results has shown that most of the tools available in Moodle are being underutilized and that the relationship between them and student's performance is very low.Resumo. Os ambientes virtuais de aprendizagem (AVA) têm contribuído para o crescimento e popularização da modalidade de ensino a distância. Tais AVAs são dotados de uma variedade de ferramentas cujo objetivoé facilitar o processo de ensino e aprendizagem. Entretanto, poucos estudos têm sido realizados para avaliar a eficácia destas ferramentas. O objetivo deste trabalhoé avaliar o uso destas ferramentas e o impacto das mesmas sobre o desempenho dos estudantes nas disciplinas. Tal avaliação foi realizada no AVA Moodle e os resultados mostram que a maioria das ferramentas disponíveis no Moodle estão sendo subutilizadas e que a relação entre elas e o desempenho dos estudantes está muito baixa. IntroduçãoO uso de ambientes virtuais de aprendizagem (AVA) tem sido um dos principais fatores que levaram ao rápido crescimento da educação a distância (EAD) [ABED 2014]. Um AVA permite que educadores compartilhem informações com alunos, produzam material de conteúdo, preparem trabalhos, testes, se envolvam em discussões, gerenciem classes a distância e permitam a aprendizagem colaborativa com fóruns, chats, armazenamento de arquivos, notícias, etc [Romero et al. 2008]. De acordo com [Magalhães et al. 2010], hoje em dia, um dos AVAs mais utilizados no mundoé o Moodle, queé uma plataforma de aprendizagem projetada para fornecer aos educadores, administradores e estudantes um sistema robusto, seguro e integrado para criar ambientes de aprendizagem personalizados [Moodle 2014]. Tem como característica manter registros detalhados de todas as atividades que os alunos realizam, gerando grandes volume de dados.Técnicas de mineração de dados podem ser aplicadas para analisar grande volume de dados gerados em AVAs. Este procedimentoé chamado EDM (Educational Data Mining). EDM está preocupado com o desenvolvimento de métodos para explorar os dados em ambientes educacionais e, através destes métodos, entender melhor os alunos e os contextos em que eles aprendem [Baker et al. 2010].
Chronic kidney disease (CKD) is a worldwide public health problem, usually diagnosed in the late stages of the disease. To alleviate such issue, investment in early prediction is necessary. The purpose of this study is to assist the early prediction of CKD, addressing problems related to imbalanced and limited-size datasets. We used data from medical records of Brazilians with or without a diagnosis of CKD, containing the following attributes: hypertension, diabetes mellitus, creatinine, urea, albuminuria, age, gender, and glomerular filtration rate. We present an oversampling approach based on manual and automated augmentation. We experimented with the synthetic minority oversampling technique (SMOTE), Borderline-SMOTE, and Borderline-SMOTE SVM. We implemented models based on the algorithms: decision tree (DT), random forest, and multi-class AdaBoosted DTs. We also applied the overall local accuracy and local class accuracy methods for dynamic classifier selection; and the k-nearest oracles-union, k-nearest oracles-eliminate, and META-DES for dynamic ensemble selection. We analyzed the models’ performances using the hold-out validation, multiple stratified cross-validation (CV), and nested CV. The DT model presented the highest accuracy score (98.99%) using the manual augmentation and SMOTE. Our approach can assist in designing systems for the early prediction of CKD using imbalanced and limited-size datasets.
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