Abstract

Health-care management systems are of great relevance now days due to provision of an easy and quick management in all aspects of a patient, not necessarily medical. Furthermore, there are more and more cases of pathologies in which diagnosis and treatment can be only carried out by using medical imaging techniques. With an ever-increasing prevalence, medical images are directly acquired in or converted into digital form, for their storage as well as subsequent retrieval and processing. Data Mining is the process of extracting information from large data sets through using algorithms and techniques drawn from the field of Statistics, Machine Learning and Data Base Management Systems. Traditional data analysis methods often involve manual work and interpretation of data which is slow, expensive and highly subjective. Authors propose a robust ontology based multidimensional data warehousing and mining approach to address the issues of organizing, reporting and documenting diabetes cases including causalities. Data mining procedures, in which map and data views depicting similarity and comparison of attributes extracted from warehouses, are used in the present studies, for understanding the ailments based on gender, age, geography, food habits and hereditary traits. Time series forecasting takes the past values of a time series and uses them to forecast the future values. In this paper, we have proposed a new algorithm for multistep ahead time series forecasting. The original time series and differenced series are classified using Competitive Learning Neural Network.