Prediction of Urinary System Disease Diagnosis: A Comparative Study of Three Decision Tree Algorithms

Data mining (DM) has a wide range of applications in the health care field. DM can be used to discover hidden patterns among different diagnoses or to predict the disease of patients based on certain number of symptoms. It can be used also to analyze the success major of a given treatment for a group of patients based on a number of characteristics and parameters available. This paper demonstrates the ability of DM to develop a prediction model for a presumptive diagnosis of two familiar urinary diseases: the acute inflammation of the urinary bladder and nephritis of renal pelvis. The dataset used in this work includes a number of characteristics, which are important in diagnosing any patient with an acute inflammation of urinary bladder or nephritis. This research evaluates the supervised machine learning algorithms Ridor, OneR, and J48 in terms of performance and accuracy to determine the best classification algorithm which will be used to develop the accurate prediction model. The decision tree (J48) shows a powerful accuracy and capability in prediction, and has been used to classify the patients' data with the proper acute inflammation diseases. The analyzed dataset has been trained using the 10-fold cross validation. The decision tree for the acute urinary bladder and nephritis has been generated.