Abstract: |
Current approaches in the field of data analysis applied to Medicine use traditional statistical methods which showed limitations Data mining consists in exploring and processing large volumes of data while the other methods are confirmatory and use structured data of often smaller sizes The main motivation of our thesis consist on the proposal of a new approach based on a hybrid Data Mining algorithm in order to extract knowledge applied to medical databases. The object of our study concerns a disease which affects about 1 % of the French population that is Schizophrenia. Conventional descriptions, codified by means of internationally recognized classifications, allowed the definition of nosographic categories of psychiatric disorders, which were however never validated by physiopathological data. It results in a considerable amount of data that needs to be optimizing both for operational and scientific purpose. It is thus necessary to use precise tools for phenotypic characterization and provide with an appreciation of the value of those variables to define possible sub-groups of the disease.
We suggest setting up knowledge extraction architecture merging Data Mining algorithms, the first part of this architecture will use the algorithm of association rules as the most relevant tool of feature selection of variables. Based on this sub-group of attributes, the second part will aim at supplying probabilistic profiles concerning phonotypical characteristics of patients suffering schizophrenia and to create a model of reliable classification by the use of the algorithms of Bayesians Networks and Neuronal Networks.
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