  In recent years, the field of medicine has been increasingly adopting
artificial intelligence (AI) technologies to provide faster and more accurate
disease detection, prediction, and assessment. In this study, we propose an
interpretable AI approach to diagnose patients with neurofibromatosis using
blood tests and pathogenic variables. We evaluated the proposed method using a
dataset from the AACR GENIE project and compared its performance with modern
approaches. Our proposed approach outperformed existing models with 99.86%
accuracy. We also conducted NF1 and interpretable AI tests to validate our
approach. Our work provides an explainable approach model using logistic
regression and explanatory stimulus as well as a black-box model. The
explainable models help to explain the predictions of black-box models while
the glass-box models provide information about the best-fit features. Overall,
our study presents an interpretable AI approach for diagnosing patients with
neurofibromatosis and demonstrates the potential of AI in the medical field.
