Novel Data Oriented Structure Learning Approach for the Diabetes Analysis

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

@article{THALJAOUI2021,
  title     = {Novel Data Oriented Structure Learning Approach for the Diabetes Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {3},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Adel THALJAOUI},
  doi       = {10.14569/IJACSA.2021.0120347},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120347}
}