An Explainable Classification Model for Chronic Kidney Disease Patients

Currently, Chronic Kidney Disease (CKD) is experiencing a globally increasing incidence and high cost to health systems. A delayed recognition leads to premature mortality due to progressive loss of kidney function. The employment of data mining to discover subtle patterns in CKD indicators would contribute to an early diagnosis. This work develops a classifier model that would support healthcare professionals in the early diagnosis of CKD patients. Through a data pipeline, an exhaustive search is performed to find the best data mining classifier with different parameters of the data preparation's sub-stages like data missing or feature selection. Therefore, Extra Trees is selected as the best classifier with a 100% and 99% of accuracy with, respectively, cross-validation technique and with new unseen data. Moreover, the 8 features selected are employed to assess the explainability of the model's results denoting which features are more relevant in the model's output.