Key result
Non-linear regression and Orange ML models both achieve ~95% accuracy predicting progressive atherosclerosis risk.
Why the study?
Studying and understanding progressive atherosclerosis and creating personalized risk assessment methods are necessary to optimize treatment and prevention.
Does a machine learning model using Orange compare favorably to a non-linear regression model in predicting the risk of progressive atherosclerosis in patients with coronary heart disease?
Cohort (n=202)
Does a machine learning model using Orange compare favorably to a non-linear regression model in predicting the risk of progressive atherosclerosis in patients with coronary heart disease?
Both non-linear regression and machine learning models using Orange demonstrate high prognostic accuracy (>95%) for predicting progressive atherosclerosis in patients with coronary heart disease.
Progressive or accelerated atherosclerosis is accompanied by unfavorable clinical outcomes. Studying and understanding this process and creating a personalized method for assessing the risk and prognosis of this disease are necessary to optimize approaches to treatment and prevention.Aim:To compare two approaches to the creation of prognostic risk model of progressive atherosclerosis: non-linear regression model of logistic type and free cross-platform visual programming system Orange method.Material and Methods.The retrospective cohort study included 202 patients with confirmed coronary heart disease: 147 men and 55 women. The mean age of the patients was 53.3 ± 7.16 years. Group 1 included patients with myocardial infarction or unstable stenocardia, emergency arterial stenting, stroke, peripheral arterial thrombosis, critical ischemia and lower extremity amputation within 2 years before inclusion in the study. Patients in the comparison group did not have these events. Predictive models of the influence of different studied parameters on the probability of rapid progression of atherosclerosis were built using factor and correlation analysis and free cross-platform Orange visual programming system.Results.The authors’ suggested approaches to the evaluation of the risk of progressive atherosclerosis have a good prognostic accuracy (sensitivity 94.1, specificity 97.0 and accuracy 95.5 coefficients, respectively) for the regression model and 0,950 (95,0%) for the machine learning model. However, the construction of the regression model is a more complex procedure compared to the second approach, where the choice of informative indicators for the prediction model is made by Orange. Nevertheless, the above two approaches can successfully complement each other, allowing to build more accurate predictive risk models.Conclusion.The proposed authors’ approaches to assessing the risk of progressive atherosclerosis have a good prognostic accuracy.
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Ложкина et al. (2023) conducted a cohort in coronary heart disease (n=202). Non-linear regression model vs. Orange visual programming system was evaluated on Prognostic accuracy. A non-linear regression model and the Orange machine learning model both demonstrated high prognostic accuracy (95.5% and 95.0%, respectively) for predicting progressive atherosclerosis risk.
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