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Recent studies demonstrated the clinical utility of seismocardiography (hereinafter SCG) signals for the detection and monitoring of cardiovascular conditions. Renewed interest in investigating the utility of SCG has been accelerated recently and benefited from new advances in low-cost lightweight sensors and machine learning methods. This article compares various machine learning algorithms (the method of nearest neighbors, the method of support vectors, decision trees, the ensemble of models) and neural networks: based on the architecture of long short-term memory and convolutional ones. An original numerical experiment was carried out using the developed mathematical software, where all of the mentioned methods and algorithms were implemented. During this study, much attention was paid to the preparation and preliminary processing of data. In particular, signal filtering is carried out using the Butterworth filter, and the issues of extracting features from the signal, which will become an input vector for machine learning algorithms, are also discussed. To compare the effectiveness of the considered models for solving the problem of diagnosing diseases, Accuracy, Recall, Sensitivity, Specificity, Precision, F1-measure, etc. are given. For each algorithm and data set, confusion matrices and ROC curves were constructed. Results of this research show that convolutional neural networks are very effective at diagnosing the states of the human cardiovascular system and supporting decision-making in cardiology and cardiac surgery.
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