Classification model for predicting inflammation of the urinary bladder and acute nephritis of the renal pelvis

Abstract

Urinary tract diseases can occur in many organs of the urinary system, such as kidneys, urinary bladder, renal pelvis, ureters, and urethra. The most common disease in the urinary system is bladder inflammation, cystitis, and acute nephritis. In this research, the classification artificial intelligent model is applied to predict 2 symptoms of inflammation of the urinary bladder and acute nephritis of the renal pelvis from 6 parameters, including body temperature of patient, nausea, lumbar pain, urinary pushing, micturition pains, and burning of the urethra. Here, the principal components analysis or PCA are also applied to identify the critical parameters employed to train the machine learning model. Here, we propose to compare several machine learning classification models and show the proper model accurately diagnosing these two symptoms.