Medical service improvement has always been a life topical problem. To de-cide it, we must continuously raise the competency of doctors on the one hand and it is necessary to develop new methods and approaches which could help take decisions concerning diagnostics (classification) of patient health conditions and concerning patient’s further treatment. At the paper the machine learning methods for patient health condition clas-sification were considered. These methods were Naive Bayes Classifier, Lin-ear Classifier, Support-vector machine, K-nearest Neighbor Classifier, Lo-gistic Regression, Decision Tree Classifier, Random Forest Classifier, Ada Boost Classifier and Artificial Neural Network. A radial basis network was chosen from the variety of artificial neural system architecture to solve clas-sification tasks. The problem of patient health conditions classification was considered for two sets of laboratory research results: on liver diseases and on urological diseases. Confusion matrixes and ROC-curves were taken to estimate classification quality of patient health conditions with above-mentioned methods.