Dental age estimation and different predictive ability of various tooth types in the Czech population: data mining methods.

Dental development is frequently used to estimate age in many anthropological specializations. The aim of this study was to extract an accurate predictive age system for the Czech population and to discover any different predictive ability of various tooth types and their ontogenetic stability during infancy and adolescence. A cross-sectional panoramic X-ray study was based on developmental stages assessment of mandibular teeth (Moorrees et al. 1963) using 1393 individuals aged from 3 to 17 years. Data mining methods were used for dental age estimation. These are based on nonlinear relationships between the predicted age and data sets. Compared with other tested predictive models, the GAME method predicted age with the highest accuracy. Age-interval estimations between the 10th and 90th percentiles ranged from -1.06 to +1.01 years in girls and from -1.13 to +1.20 in boys. Accuracy was expressed by RMS error, which is the average deviation between estimated and chronological age. The predictive value of individual teeth changed during the investigated period from 3 to 17 years. When we evaluated the whole period, the second molars exhibited the best predictive ability. When evaluating partial age periods, we found that the accuracy of biological age prediction declines with increasing age (from 0.52 to 1.20 years in girls and from 0.62 to 1.22 years in boys) and that the predictive importance of tooth types changes, depending on variability and the number of developmental stages in the age interval. GAME is a promising tool for age-interval estimation studies as they can provide reliable predictive models.

Meta-learning approach to neural network optimization.

Optimization of neural network topology, weights and neuron transfer functions for given data set and problem is not an easy task. In this article, we focus primarily on building optimal feed-forward neural network classifier for i.i.d. data sets. We apply meta-learning principles to the neural network structure and function optimization. We show that diversity promotion, ensembling, self-organization and induction are beneficial for the problem. We combine several different neuron types trained by various optimization algorithms to build a supervised feed-forward neural network called Group of Adaptive Models Evolution (GAME). The approach was tested on a large number of benchmark data sets. The experiments show that the combination of different optimization algorithms in the network is the best choice when the performance is averaged over several real-world problems.

Comparison of seven approaches for holter ECG clustering and classification.

In this work we present a comparative study, testing selected methods for clustering and classification of holter electrocardiogram (ECG). More specifically we focus on the task of discriminating between normal 'N' beats and premature ventricular 'V' beats Some of the tested methods represent the state of the art in pattern analysis, while others are novel algorithms developed by us. All the algorithms were tested on the same datasets, namely the MIT-BIH and the AHA databases. The results for all the employed methods are compared and evaluated using the measures of sensitivity and specificity.