A hybrid data mining model for diagnosis of patients with clinical suspicion of dementia.

BACKGROUND AND OBJECTIVE: Given the phenomenon of aging population, dementias arise as a complex health problem throughout the world. Several methods of machine learning have been applied to the task of predicting dementias. Given its diagnostic complexity, the great challenge lies in distinguishing patients with some type of dementia from healthy people. Particularly in the early stages, the diagnosis positively impacts the quality of life of both the patient and the family. This work presents a hybrid data mining model, involving the mining of texts integrated to the mining of structured data. This model aims to assist specialists in the diagnosis of patients with clinical suspicion of dementia. METHODS: The experiments were conducted from a set of 605 medical records with 19 different attributes about patients with cognitive decline reports. Firstly, a new structured attribute was created from a text mining process. It was the result of clustering the patient's pathological history information stored in an unstructured textual attribute. Classification algorithms (naïve bayes, bayesian belief networks and decision trees) were applied to obtain Alzheimer's disease and mild cognitive impairment predictive models. Ensemble methods (Bagging, Boosting and Random Forests) were used in order to improve the accuracy of the generated models. These methods were applied in two datasets: one containing only the original structured data; the other containing the original structured data with the inclusion of the new attribute resulting from the text mining (hybrid model). RESULTS: The models' accuracy metrics obtained from the two different datasets were compared. The results evidenced the greater effectiveness of the hybrid model in the diagnostic prediction for the pathologies of interest. CONCLUSIONS: When analysing the different methods of classification and clustering used, the better rates related to the precision and sensitivity of the pathologies under study were obtained with hybrid models with support of ensemble methods.

Sistema preditivo para a doença de Alzheimer na triagem clínica / System predictive for Alzheimer's disease in clinical trial / Predictivo Sistema para la enfermedad de Alzheimer en ensayo clínico

Objetivo: Descrever uma aplicação que, utilizando-se de técnicas de mineração de dados, visa auxiliar os especialistas no processo de diagnóstico de pacientes com suspeita clínica de Alzheimer atendidos pelo Centro de Alzheimer e Parkinson no município de Campos dos Goytacazes/RJ. Método: Aplicação de técnicas relacionadas à etapa de pré-processamento dos dados, de classificação (naïve bayes, redes bayesianas e árvores de decisão) com avaliação dos resultados a partir do uso da validação cruzada estratificada k-fold, cujas implementações estão disponíveis na ferramenta Weka. Resultado: Observou-se os resultados numéricos dos modelos de acordo com as métricas: acurácia, taxa de erro, sensibilidade, taxa de falsos positivos e taxa de falsos negativos, obtendo-se as taxas de 73,8%, 26,2%, 76,3%, 27,4%, 23,7%, respectivamente. Conclusão: Verificou-se que os classificadores bayesianos, em especial redes bayesianas, apresentaram os melhores resultados para o diagnóstico da doença de Alzheimer a partir das métricas supracitadas.

Objective: Describe an application that uses data mining techniques and aims to support those specialists in the Alzheimer diagnostic process which assist patients in the Alzheimer's and Parkinson's Center, located in the city of Campos dos Goytacazes, Brazil. Methods: Application of methods related with data pre-processing, classification (naïve bayes, bayesian networks and decision trees) using k-fold method for results evaluation, whose implementations are available in the Weka tool. Results: The obtained results related to the accuracy of the models were: accuracy - 73.8%; error rate ­ 26.2%; sensitivity ­ 76.3%; false positive rate - 27.4%; and false negative rate - 23.7%. Conclusion: The Bayesian classifiers, more specifically Bayesian networks, presented best results for the diagnosis of Alzheimer.

Objetivo: Describir una aplicación que utiliza técnicas de minería de datos, con objetivo de ayudar a los expertos en el diagnóstico de pacientes con sospecha de enfermedad de Alzheimer, que son atendidos en el Centro de Alzheimer y Parkinson en el municipio de Campos dos Goytacazes, Brasil. Método: Aplicación de técnicas para el procesamiento previo de los datos, la clasificación (naïve Bayes, redes bayesianas y rboles de decisión), aplicando el método k-fold en la evaluación de los resultados, cuyas implementaciones están disponibles en la herramienta Weka. Resultado: Los resultados obtenidos en relación con la exactitud de los modelos fueron: precisión ­ 73,8%; tasa de error - 26,2%; Sensibilidad ­ 76.3%; tasa de falsos positivos - 27,4%; y tasa de falsos negativos ­ 23,7%. Conclusión: Los clasificadores bayesianos, más concretamente redes bayesianas, presentaran mejores resultados para el diagnóstico de la enfermedad de Alzheimer.

RFID technology for hazardous waste management and tracking.

The illegal dumping of hazardous waste is one of the most concerning occurrences related to illegal waste activities. The waste management process is quite vulnerable, especially when it comes to assuring the right destination for the delivery of the hazardous waste. The purpose of this paper is to present a new system design and prototype for applying the RFID technology so as to guarantee the correct destination for the hazardous waste delivery. The aim of this innovative approach, compared with other studies that employ the same technology to the waste disposal process, is to focus on the certification that the hazardous waste will be delivered to the right destination site and that no inappropriate disposal will occur in the transportation stage. These studies were carried out based on data collected during visits to two hazardous waste producer companies in Brazil, where the material transportation and delivery to a company in charge of the waste disposal were closely monitored.