Statistical Procedures and Data Mining Approaches Sample Clauses

Statistical Procedures and Data Mining Approaches. Temporal data mining, time-series prediction, sequence classification methods, clustering time- series data, and temporal association rules will be used to develop and validate the predictive model. Mediation, moderation and moderated mediation analyses will have a central role in the statistical methodology. In addition, any other methodology appropriate for time series prediction including autoregressive models, chaotic time series, Markov chains and deep learning will be considered for the optimal prediction of resilience in Βounce. Because the performance of predictive models can be considerably deteriorated when non- relevant features (i.e. variables) are included during the training phase of the model, feature selection will take place prior to model building. Different feature selection techniques or combinations of them will be applied, namely filter methods, wrapper methods and embedded methods. Filter type methods that will be considered in the present study also include well- known statistical tests and procedures such as Student's t-test, Analysis of variance (ANOVA), Xxxx–Xxxxxxx U test, Kruskal-Wallis test, correlation, regression analyses etc. Given the temporal/sequential nature of data, BOUNCE will also make use of methodologies specific for time series analysis (e.g. repeated measures, autocorrelation analysis etc). The methodological approach is described below:  Univariate (e.g. t-test, chi-squared test, Xxxx–Xxxxxxx U test, Xxxxxxxx'x rank correlation coefficient etc.) and multivariate techniques (e.g. logistic regression, correlation-based feature selection, sequential forward selection, sequential backward elimination, decision trees, naive Bayes etc.) will be performed to identify features of importance.  Intelligent pattern recognition analysis of an individual’s context will be applied to allow the identification of established behaviors and eventually, cause and effect relationships.  Given the sequential nature of recorded data, association analysis techniques, able to handle both co-occurrence and dynamic relationships in multivariate time series data, will be utilized.  Temporal data mining will enable the identification of dynamic patterns or predictive rules in long-term trajectories and, eventually, will allow drawing conclusions regarding the associations between the patient’s context - indicating resilience to BC - and the clinical health outcomes, and vice versa.  The identification of groups of patients with simila...