Novel Data Oriented Structure Learning Approach for the Diabetes Analysis

Diabetes mellitus is considered a significant disease an ever rising epidemic. Accordingly this represents worldwide public-health-crisis. Several classification techniques have been recently employed for diabetes diagnosis, however only few researches dedicated to facilitating its analysis based on knowledge representation using probabilistic modelling. Bayesian Network as graphical model one of the most effective classification. (BN) widely in several domains like risk analysis, medicine, bioinformatics and security. This formalism reason under uncertainty. The construction BN goes through two learning phases structure parameter. first phase skeleton has assessed complex problem (NP-hard problem). Accordingly, methods introduced amongst which score algorithms that are powerful learning. In paper, we introduce novel algorithm graph theory information combination. proposed called GIT Parents children detection addition, evaluate obtained results reference networks, prove efficiency terms accuracy. Furthermore, apply our real field, especially detecting interesting dependencies useful analysis.