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In this study, we examine the applicability of association rules for analysing high-dimensional data concerning age-related hearing impairment (ARHI). The ARHI data of the study contain hundreds of variables concerning phenotype, genotype and environmental factors. The number of association rules produced from the data is too large for manual exploration in the raw and furthermore, the rules are overlapping. Thus, the focus of our study is to develop an approach to cluster association rules into subsets and to summarise and represent the found rule subsets for easier exploration of rules. The results show that it is possible to efficiently extract rules representing interesting environmental factor-gene or gene-gene interactions. Finding suitable parameters for the association rule mining and the possibility to post-process the mined rules is essential. The developed approach facilitates rule exploration by grouping rules with items concerning the same phenomenon to the same subset and byrevealing overlapping rules. [ABSTRACT FROM AUTHOR]

Induced pluripotent stem cell (iPSC) lines derived from skin fibroblasts of patients suffering from cardiac disorders were differentiated to cardiomyocytes and used to generate a data set of Ca(2+) transients of 136 recordings. The objective was to separate normal signals for later medical research from abnormal signals. We constructed a signal analysis procedure to detect peaks representing calcium cycling in signals and another procedure to classify them into either normal or abnormal peaks. Using machine learning methods we classified signals into normal or abnormal signals on the basis of peak findings in them. We compared classification results obtained to those made visually by an expert biotechnologist who assessed the signals independent of the computer method. Classification accuracies of around 85% indicated high congruence between two modes denoting the high capability and usefulness of computer based processing for the present data.

In this paper we applied altogether 13 classification methods to otoneurological disease classification. The main point was to use Half-Against-Half (HAH) architecture in classification. HAH structure was used with Support Vector Machines (SVMs), k-Nearest Neighbour (k-NN) method and Naïve Bayes (NB) methods. Furthermore, Multinomial Logistic Regression (MNLR) was tested for the dataset. HAH-SVM with the linear kernel achieved clearly the best accuracy being 76.9% which was a good result with the dataset tested. From the other classification methods HAH-k-NN with cityblock metric, HAH-NB and MNLR methods achieved above 60% accuracy. Around 77% accuracy is a good result compared to previous researches with the same dataset.

We studied how the splitting of a multi-class classification problem into multiple binary classification tasks, like One-vs-One (OVO) and One-vs-All (OVA), affects the predictive accuracy of disease classes. Classifiers were tested with an otoneurological data using 10-fold cross-validation 10 times with k-Nearest Neighbour (k-NN) method and Support Vector Machines (SVM). The results showed that the use of multiple binary classifiers improves the classification accuracies of disease classes compared to one multi-class classifier. In general, OVO classifiers worked out better with this data than OVA classifiers. Especially, the OVO with k-NN yielded the highest total classification accuracies.