Key result
Machine learning models show high predictive accuracy for classifying cardiovascular disease and heart attack risk.
Why the study?
This study was conducted to evaluate the application of machine learning methods for identifying cardiovascular diseases in patients and assessing the risk of heart attack.
Do machine learning models (logistic regression, random forest, support vector machine, and artificial neural network) accurately identify cardiovascular diseases and assess the risk of heart attack in patients?
Do machine learning models (logistic regression, random forest, support vector machine, and artificial neural network) accurately identify cardiovascular diseases and assess the risk of heart attack in patients?
Machine learning models using principal component analysis demonstrated high predictive power for non-invasive diagnostics, patient classification, and prognosis of cardiovascular diseases.
ML models with PCA may aid CVD classification; leaves open prospective validation before clinical use.
This paper examines the application of machine learning methods to identifying cardiovascular diseases in patients and assessing the risk of heart attack. Four machine learning models were used to classify patients by health status: logistic regression, random forest, support vector machine, and artificial neural network. The selection of significant factors was carried out using the principal component analysis method. Optimal model architectures are proposed and hyperparameters are selected to ensure high classification accuracy. All models demonstrated high predictive power, as measured by precision, recall, F1 score, and AUC-ROC. The obtained results indicate the possibility of using the proposed approach as an effective tool for non-invasive diagnostics, patient classification and prognosis of cardiovascular diseases.
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Khusnutdinoff et al. (2026) studied Cardiovascular disease and heart attack risk. Machine learning models (logistic regression, random forest, support vector machine, artificial neural network) with principal component analysis vs. Comparative analysis among the 4 machine learning models was evaluated on Classification of cardiovascular disease and heart attack risk (precision, recall, F1 score, and AUC-ROC). Four machine learning models (logistic regression, random forest, SVM, and ANN) demonstrated high predictive power for classifying cardiovascular disease, though specific metrics were not reported.
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