  Cardiovascular diseases are widespread among patients with chronic
noncommunicable diseases and are one of the leading causes of death, including
in the working age. The article presents the relevance of the development and
application of patient-oriented systems, in which machine learning (ML) is a
promising technology that allows predicting cardiovascular diseases. Automated
machine learning (AutoML) makes it possible to simplify and speed up the
process of developing AI/ML applications, which is key in the development of
patient-oriented systems by application users, in particular medical
specialists. The authors propose a framework for the application of automatic
machine learning and three scenarios that allowed for data combining five data
sets of cardiovascular disease indicators from the UCI Machine Learning
Repository to investigate the effectiveness in detecting this class of
diseases. The study investigated one AutoML model that used and optimized the
hyperparameters of thirteen basic ML models (KNeighborsUnif, KNeighborsDist,
LightGBMXT, LightGBM, RandomForestGini, RandomForestEntr, CatBoost,
ExtraTreesGini, ExtraTreesEntr, NeuralNetFastA, XGBoost, NeuralNetTorch,
LightGBMLarge) and included the most accurate models in the weighted ensemble.
The results of the study showed that the structure of the AutoML model for
detecting cardiovascular diseases depends not only on the efficiency and
accuracy of the basic models used, but also on the scenarios for preprocessing
the initial data, in particular, on the technique of data normalization. The
comparative analysis showed that the accuracy of the AutoML model in detecting
cardiovascular disease varied in the range from 87.41% to 92.3%, and the
maximum accuracy was obtained when normalizing the source data into binary
values, and the minimum was obtained when using the built-in AutoML technique.
