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Automatic diagnosis of primary headaches by machine learning methods

100%

Krawczyk B. , Simić D. , Simić S. , Woźniak M.

Open Medicine

2013

tom 8

nr 2

157-165

Primary headaches are common disease of the modern society and it has high negative impact on the productivity and the life quality of the affected person. Unfortunately, the precise diagnosis of the headache type is hard and usually imprecise, thus methods of headache diagnosis are still the focus of intense research. The paper introduces the problem of the primary headache diagnosis and presents its current taxonomy. The considered problem is simplified into the three class classification task which is solved using advanced machine learning techniques. Experiments, carried out on the large dataset collected by authors, confirmed that computer decision support systems can achieve high recognition accuracy and therefore be a useful tool in an everyday physician practice. This is the starting point for the future research on automation of the primary headache diagnosis.

Pattern recognition approach to classifying CYP 2C19 isoform

86%

Krawczyk B.

Open Medicine

2012

tom 7

nr 1

38-44

In this paper a pattern recognition approach to classifying quantitative structure-property relationships (QSPR) of the CYP2C19 isoform is presented. QSPR is a correlative computer modelling of the properties of chemical molecules and is widely used in cheminformatics and the pharmaceutical industry. Predicting whether or not a particular chemical will be metabolized by 2C19 is of primary importance to the pharmaceutical industry. This task poses certain challenges. First of all analyzed data are characterized by a significant biological noise. Additionally the training set is unbalanced, with objects from negative class outnumbering the positives four times. Presented solution deals with those problems, additionally incorporating a throughout feature selection for improving the stability of received results. A strong emphasis is put on the outlier detection and proper model validation to achieve the best predictive power.