Abstract. This paper examines methods for the automatic analysis of biomedical images in cardiology using machine learning techniques. The relevance of the study is determined by the need to improve the accuracy of cardiovascular disease diagnosis through automated processing of heart and capillary images. The study focuses on analyzing data obtained from digital microscopes and electrocardiographs, emphasizing the identification of key diagnostic features. The proposed approach includes image preprocessing, noise removal, feature extraction, and classification based on principal component analysis (PCA) and neural network models. The preprocessing phase involves image filtering, segmentation, and data normalization. The study employs machine learning classification algorithms and deep learning techniques adapted for medical image analysis. Performance evaluation criteria and training parameters are examined to enhance diagnostic efficiency and ensure model generalization. Particular attention is paid to the biological safety aspects related to biomedical data processing, including personal data protection and classification accuracy. The study also evaluates the robustness of different models to variations in image quality and external factors. Additionally, it discusses the integration of machine learning-based image analysis with medical decision support systems for improved diagnostic precision. The paper analyzes the limitations of existing algorithms and suggests directions for their further improvement, including adaptation to different types of data and complex clinical scenarios. Future research perspectives include the optimization of feature extraction methods, refinement of classification algorithms, and the development of hybrid models that combine multiple approaches to improve diagnostic accuracy. Thus, the presented review of machine learning methods and biomedical image analysis algorithms identifies the most effective approaches for automated cardiovascular disease diagnosis and highlights the prospects for further development of intelligent medical systems.

Keywords: machine learning, artificial intelligence, neural networks, biomedical image processing, cardiology.