Résumé:
hronic diseases, especially those affecting the liver, pose a major challenge to global heal thcare systems. In this dissertation, we have explored various aspects from prediction to simula ting the migration between stages of these chronic conditions. Utilizing advanced data analysis
and machine learning techniques, our study focuses on four key aspects : improving predic tion, feature selection, model optimization, and meta-classification, along with simulating the
migration between disease stages for preventive purposes. At each stage, rigorous experiments
were conducted to validate our methodology. The results confirm the crucial importance of
prediction in anticipating disease progression, as well as the effectiveness of feature selection
and model optimization in enhancing prediction performance. Meta-classification, by combi ning predictions from different models, enhances result reliability. Furthermore, simulating the
migration between stages provides a better understanding of disease progression dynamics
