This article substantiates the need to use data from an integrated electronic medical record of a patient to assess the risk of cancer. An exploratory analysis of the data of the integrated electronic medical record of patients in the Bryansk region who received a diagnosis of "malignant neoplasm" is being carried out. The influence of the patient's age on the risk of oncological diseases is evaluated by the example of the nosologies C50, C61. Provides an overview of the capabilities of the Auto ML Libraries and their limitations. The article describes the result of constructing models for assessing the risk of oncological diseases based on the ML.NET and Auto-WEKA libraries. It is concluded that it is impossible to constructing models for assessing the risk of oncological diseases based on the data of an integrated electronic medical record using Auto ML libraries without preliminary preparation and preprocessing of data. And since it is required to constructing separate models for each nosology and regular retraining of these models, it is advisable to develop an add-on over the Auto ML libraries that will extract and convert the data of the integrated electronic medical record into a form suitable for analysis. In addition, to improve the quality of the model, it is advisable to use patient history data, data obtained after vectorization of laboratory tests, aggregated data on visits to specialized specialists and related diagnoses, data from online patient questionnaires filled out during the course of medical examination, as well as data on environmental pollution.