How to explain a machine learning model: HbA1c classification example

Aim: Machine learning tools have various applications in healthcare. However, the implementation of developed models is still limited because challenges. One most important problems lack explainability machine models. Explainability refers to capacity reveal reasoning and logic behind decisions made by AI systems, making it straightforward for human users understand process how system arrived at a specific outcome. The study aimed compare performance different model-agnostic explanation methods using two ML created HbA1c classification.
 Material Method: H2O AutoML engine was used development (Gradient boosting (GBM) default random forests (DRF)) 3,036 records from NHANES open data set. Both global local methods, including metrics, feature analysis Partial dependence, Breakdown Shapley additive plots were utilized 
 Results: While both GBM DRF similar such as mean per class error area under receiver operating characteristic curve, they had slightly variable importance. Local also showed contributions features. Conclusion: This evaluated significance explainable techniques comprehending complicated their role incorporating results indicate that although there are limitations current particularly clinical use, offer glimpse into evaluating model can be enhance or