Abstract: Systems and methods are disclosed for diagnosis and treatment of urinary tract symptoms into machine learning based clusters. In some examples, a diagnostic questionnaire is processed by a machine learning model to evaluate a patient's urinary tract health condition and determine a diagnosis based on one or more indications of urinary tract health of the patient. In one example, the machine learning model is trained using datasets labelled according to one or more diagnostic clusters generated by an unsupervised learning model, such as a clustering model. In some examples, a measure of severity of the diagnosis is output by the machine learning model or a second machine learning model.