Application of Machine Learning to Determine the Factors Affecting Deterioration in Patients with Chronic Kidney Disease

- Herwanto, Ali Khumaidi


Hospital databases generally contain large amounts of data and various, but it has not been used optimally. It needs a technique that can utilize mountains of data into strategically valuable information. This paper will investigate ways to use hospital data to help determine the factors that influence the deterioration in patients with chronic kidney disease. The criteria for the selected patients were patients with a diagnosis of chronic kidney disease and chemotherapy treatment at least once. Three hundred seventy-six patients met these criteria. Subsequently, observation the patient's treatment course for three years. Ninety patients died in the hospital during that period. All the results of patients' blood tests were collected for further analysis. In forming the classification model, there are three stages carried out. The first stage deals with diverse, incomplete, and inconsistent data. Then through the process of changing continuous data into categorical data, each variable is classified into several categories. The next stage is to create a predictive model to determine the factors that influence the deterioration in patients with kidney failure using the Random Forest, Logistic Regression, and Decision Tree algorithms. Information of the classification model, 12 variables were selected, namely age, sex, and the results of clinical pathology laboratory examinations-Ureum, Thrombocyte, Natrium, Creatinine, Chloride, Kalium, Hemoglobin, Hematocrit, and Leukocytes. The three algorithms can classify training data with an accuracy of 98% (Random Forest), 83% (Logistic Regression), 98% (ID3).


Chronic kidney disease; machine learning; classification; discretization; decision tree.

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