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Machine Learning (ML) models are often complex and difficult to interpret dueto their 'black-box' characteristics. Interpretability of a ML model is usuallydefined as the degree to which a human can understand the cause of decisionsreached by a ML model. Interpretability is of extremely high importance in manyfields of healthcare due to high levels of risk related to decisions based onML models. Calibration of the ML model outputs is another issue oftenoverlooked in the application of ML models in practice. This paper representsan early work in examination of prediction model calibration impact on theinterpretability of the results. We present a use case of a patient in diabetesscreening prediction scenario and visualize results using three differenttechniques to demonstrate the differences between calibrated and uncalibratedregularized regression model.

Local Interpretability of Calibrated Prediction Models: A Case of Type 2 Diabetes Mellitus Screening Test