The MARK-AGE extended database: data integration and pre-processing.

MARK-AGE is a recently completed European population study, where bioanalytical and anthropometric data were collected from human subjects at a large scale. To facilitate data analysis and mathematical modelling, an extended database had to be constructed, integrating the data sources that were part of the project. This step involved checking, transformation and documentation of data. The success of downstream analysis mainly depends on the preparation and quality of the integrated data. Here, we present the pre-processing steps applied to the MARK-AGE data to ensure high quality and reliability in the MARK-AGE Extended Database. Various kinds of obstacles that arose during the project are highlighted and solutions are presented.

Knowledge-based and data-driven models in arrhythmia fuzzy classification.

OBJECTIVES: Fuzzy rules automatically derived from a set of training examples quite often produce better classification results than fuzzy rules translated from medical knowledge. This study aims to investigate the difference in domain representation between a knowledge-based and a data-driven fuzzy system applied to an electrocardiography classification problem. METHODS: For a three-class electrocardiographic arrhythmia classification task a set of fifteen fuzzy rules is derived from medical expertise on the basis of twelve electrocardiographic measures. A second set of fuzzy rules is automatically constructed on thirty-nine MIT-BIH database's records. The performances of the two classifiers on thirteen different records are comparable and up to a certain extent complementary. The two fuzzy models are then analyzed, by using the concept of information gain to estimate the impact of each ECG measure on each fuzzy decision process. RESULTS: Both systems rely on the beat prematurity degree and the QRS complex width and neglect the P wave existence and the ST segment features. The PR interval is not well characterized across the fuzzy medical rules while it plays an important role in the data-driven fuzzy system. The T wave area shows a higher information gain in the knowledge based decision process, and is not very much exploited by the data-driven system. CONCLUSIONS: The main difference between a human designed and a data driven ECG arrhythmia classifier is found about the PR interval and the T wave.

Input features' impact on fuzzy decision processes.

Many real-world applications have very high dimensionality and require very complex decision borders. In this case, the number of fuzzy rules can proliferate, and the easy interpretability of fuzzy models can progressively disappear. An important part of the model interpretation lies on the evaluation of the effectiveness of the input features on the decision process. In this paper, we present a method that quantifies the discriminative power of the input features in a fuzzy model. The separability among all the rules of the fuzzy model produces a measure of the information available in the system. Such measure of information is calculated to characterize the system before and after each input feature is used for classification. The resulting information gain quantifies the discriminative power of that input feature. The comparison among the information gains of the different input features can yield better insights into the selected fuzzy classification strategy, even for very high dimensional cases, and can lead to a possible reduction of the input space dimension. Several artificial and real-world data analysis scenarios are reported as examples in order to illustrate the characteristics and potentialities of the proposed method.