Explainable machine learning model for predicting the severity level of chronic kidney disease
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This cross-sectional survey was conducted in Felegehiwot hospital, Kidanemihret Speciality Clinic, and Minilik II Hospital and encompassed data from 2012 up to 2016 data collection sources to gather necessary data (1,325) for prediction. Python software was used for analysis and prediction. We use three steps to deal with missing values in our dataset. for attributes like age, blood pressure, chloride, sodium, potassium, blood urea nitrogen,
creatinine, white blood cell count, red blood cell count, hemoglobin, mean cell volume, and platelets.