AIMS: To analyse the predictive capacity of 15 machine learning methods for estimating cardiovascular risk in a cohort and to compare them with other risk scales. METHODS: We calculated cardiovascular risk by means of 15 machine-learning methods and using the SCORE and REGICOR scales and in 38 527 patients in the Spanish ESCARVAL RISK cohort, with 5-year follow-up. We considered patients to be at high risk when the risk of a cardiovascular event was over 5% (according to SCORE and machine learning methods) or over 10% (using REGICOR). The area under the receiver operating curve (AUC) and the C-index were calculated, as well as the diagnostic accuracy rate, error rate, sensitivity, specificity, positive and negative predictive values, positive likelihood ratio, and number needed to treat to prevent a harmful outcome. RESULTS: The method with the greatest predictive capacity was quadratic discriminant analysis, with an AUC of 0.7086, followed by Naive Bayes and neural networks, with AUCs of 0.7084 and 0.7042, respectively. REGICOR and SCORE ranked 11th and 12th, respectively, in predictive capacity, with AUCs of 0.63. Seven machine learning methods showed a 7% higher predictive capacity (AUC) as well as higher sensitivity and specificity than the REGICOR and SCORE scales. CONCLUSIONS: Ten of the 15 machine learning methods tested have a better predictive capacity for cardiovascular events and better classification indicators than the SCORE and REGICOR risk assessment scales commonly used in clinical practice in Spain. Machine learning methods should be considered in the development of future cardiovascular risk scales.
AIMS: To analyse the predictive capacity of 15 machine learning methods for estimating cardiovascular risk in a cohort and to compare them with other risk scales. METHODS: We calculated cardiovascular risk by means of 15 machine-learning methods and using the SCORE and REGICOR scales and in 38 527 patients in the Spanish ESCARVAL RISK cohort, with 5-year follow-up. We considered patients to be at high risk when the risk of a cardiovascular event was over 5% (according to SCORE and machine learning methods) or over 10% (using REGICOR). The area under the receiver operating curve (AUC) and the C-index were calculated, as well as the diagnostic accuracy rate, error rate, sensitivity, specificity, positive and negative predictive values, positive likelihood ratio, and number needed to treat to prevent a harmful outcome. RESULTS: The method with the greatest predictive capacity was quadratic discriminant analysis, with an AUC of 0.7086, followed by Naive Bayes and neural networks, with AUCs of 0.7084 and 0.7042, respectively. REGICOR and SCORE ranked 11th and 12th, respectively, in predictive capacity, with AUCs of 0.63. Seven machine learning methods showed a 7% higher predictive capacity (AUC) as well as higher sensitivity and specificity than the REGICOR and SCORE scales. CONCLUSIONS: Ten of the 15 machine learning methods tested have a better predictive capacity for cardiovascular events and better classification indicators than the SCORE and REGICOR risk assessment scales commonly used in clinical practice in Spain. Machine learning methods should be considered in the development of future cardiovascular risk scales.
Authors: Ankush Jamthikar; Deep Gupta; Luca Saba; Narendra N Khanna; Tadashi Araki; Klaudija Viskovic; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; Vijay Viswanathan; Aditya Sharma; Andrew Nicolaides; George D Kitas; Jasjit S Suri Journal: Cardiovasc Diagn Ther Date: 2020-08
Authors: Demilade A Adedinsewo; Amy W Pollak; Sabrina D Phillips; Taryn L Smith; Anna Svatikova; Sharonne N Hayes; Sharon L Mulvagh; Colleen Norris; Veronique L Roger; Peter A Noseworthy; Xiaoxi Yao; Rickey E Carter Journal: Circ Res Date: 2022-02-17 Impact factor: 23.213
Authors: Rajkumar Gangappa Nadakinamani; A Reyana; Sandeep Kautish; A S Vibith; Yogita Gupta; Sayed F Abdelwahab; Ali Wagdy Mohamed Journal: Comput Intell Neurosci Date: 2022-01-11