The Big Data mining to improve medical diagnostics quality

The paper offers a method of the big data mining to solve problems of identification of cause-and-effect relationships in changing diagnostic information on medical images with different kinds of diseases. As integrated indices of the fundus vessels and coronary heart blood vessels we have used a global set of geometric features which is supposed to be a rather complete characteristic of diagnostic images and allows to make a successful diagnosis of vascular malformations. To evaluate informativity of vascular diagnostic features based on a classification efficiency criterion and in order to form new features required to improve a diagnostics quality a method of discriminative analysis of sample data has been considered. A filtration method of invalid data is proposed using a clustering algorithm to improve a performance quality of the developed algorithm of the discriminative analysis of feature vectors.