Towards transparent machine learning models using feature sensitivity algorithm - Ali A. Abaker; Fakhreldeen A. Saeed (Jurnal Informatika, 2020)
Despite advances in health care, diabetic ketoacidosis (DKA) remains a potentially serious risk for diabetes. Directing diabetes patients to the appropriate unit of care is very critical for both lives and healthcare resources. Missing data occurs in almost all machine learning models, especially in production. Missing data can reduce the predictive power and produce biased estimates of models. Estimating a missing value around a 50 percent probability may lead to a completely different decision. The objective of this paper was to introduce a feature sensitivity score using the proposed feature sensitivity algorithm. The data were electronic health records contained 644 records and 28 attributes. We designed a model using a random forest classifier that predicts the likelihood of a developing patient DKA at the time of admission. The model achieved an accuracy of 80 percent using five attributes; this new model has fewer features than any model mentioned in the literature review. Also, Feature sensitivity score (FSS) was introduced, which identifies within feature sensitivity; the proposed algorithm enables physicians to make transparent, and accurate decisions at the time of admission. This method can be applied to different diseases and datasets.