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Artificial neural network versus multiple logistic function to predict 25-year coronary heart disease mortality in the Seven Countries Study.
Eur J Cardiovasc Prev Rehabil. 2009 Oct; 16(5):583-91.EJ

Abstract

AIMS AND METHODS

We investigated 12 763 men enrolled in the Seven Countries Study and 25-year coronary heart disease (CHD) mortality to compare the predictive discrimination of the multilayer perceptron (MLP) neural network versus multiple logistic function based on four standard, continuous risk factors, selected a priori. The patients were grouped according to geographical distribution, which also parallels CHD mortality risk. Logistic model solutions were estimated for each geographic area. Training neural network models were estimated in one high risk (US) and one low risk (Italy) population and each was rerun in each nonindex population.

RESULTS

CHD mortality prediction by training MLP neural network or multiple logistic function had similar (0.669-0.699) receiver operating characteristic area under the curve (AUC). The rerun of MLP neural network models derived from the US and Italy yielded comparable AUC similar to the logistic solutions in Northern and Eastern Europe, but higher AUC in two areas [0.633 (logistic) vs. 0.665 or 0.666 (neural network: P<0.05) in Southern Europe and 0.676 (logistic) vs. 0.725 or 0.737 (neural network: P<0.01) in Japan].

CONCLUSION

This is the first investigation performed on epidemiological data to suggest a good performance in predicting long-term CHD mortality, on the basis of few continuous risk factors, of a training neural network model that could be rerun on different populations with satisfactory findings.

Authors+Show Affiliations

UOC di Biotecnologie Applicate alle Malattie Cardiovascolari, Department of the Heart and Great Vessels Attilio Reale, University of La Sapienza bAssociazione per la Ricerca Cardiologica, Rome, Italy. paoloemilio.puddu@uniroma1.itNo affiliation info available

Pub Type(s)

Comparative Study
Journal Article
Multicenter Study

Language

eng

PubMed ID

19602982

Citation

Puddu, Paolo Emilio, and Alessandro Menotti. "Artificial Neural Network Versus Multiple Logistic Function to Predict 25-year Coronary Heart Disease Mortality in the Seven Countries Study." European Journal of Cardiovascular Prevention and Rehabilitation : Official Journal of the European Society of Cardiology, Working Groups On Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology, vol. 16, no. 5, 2009, pp. 583-91.
Puddu PE, Menotti A. Artificial neural network versus multiple logistic function to predict 25-year coronary heart disease mortality in the Seven Countries Study. Eur J Cardiovasc Prev Rehabil. 2009;16(5):583-91.
Puddu, P. E., & Menotti, A. (2009). Artificial neural network versus multiple logistic function to predict 25-year coronary heart disease mortality in the Seven Countries Study. European Journal of Cardiovascular Prevention and Rehabilitation : Official Journal of the European Society of Cardiology, Working Groups On Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology, 16(5), 583-91. https://doi.org/10.1097/HJR.0b013e32832d49e1
Puddu PE, Menotti A. Artificial Neural Network Versus Multiple Logistic Function to Predict 25-year Coronary Heart Disease Mortality in the Seven Countries Study. Eur J Cardiovasc Prev Rehabil. 2009;16(5):583-91. PubMed PMID: 19602982.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Artificial neural network versus multiple logistic function to predict 25-year coronary heart disease mortality in the Seven Countries Study. AU - Puddu,Paolo Emilio, AU - Menotti,Alessandro, PY - 2009/7/16/entrez PY - 2009/7/16/pubmed PY - 2010/3/12/medline SP - 583 EP - 91 JF - European journal of cardiovascular prevention and rehabilitation : official journal of the European Society of Cardiology, Working Groups on Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology JO - Eur J Cardiovasc Prev Rehabil VL - 16 IS - 5 N2 - AIMS AND METHODS: We investigated 12 763 men enrolled in the Seven Countries Study and 25-year coronary heart disease (CHD) mortality to compare the predictive discrimination of the multilayer perceptron (MLP) neural network versus multiple logistic function based on four standard, continuous risk factors, selected a priori. The patients were grouped according to geographical distribution, which also parallels CHD mortality risk. Logistic model solutions were estimated for each geographic area. Training neural network models were estimated in one high risk (US) and one low risk (Italy) population and each was rerun in each nonindex population. RESULTS: CHD mortality prediction by training MLP neural network or multiple logistic function had similar (0.669-0.699) receiver operating characteristic area under the curve (AUC). The rerun of MLP neural network models derived from the US and Italy yielded comparable AUC similar to the logistic solutions in Northern and Eastern Europe, but higher AUC in two areas [0.633 (logistic) vs. 0.665 or 0.666 (neural network: P<0.05) in Southern Europe and 0.676 (logistic) vs. 0.725 or 0.737 (neural network: P<0.01) in Japan]. CONCLUSION: This is the first investigation performed on epidemiological data to suggest a good performance in predicting long-term CHD mortality, on the basis of few continuous risk factors, of a training neural network model that could be rerun on different populations with satisfactory findings. SN - 1741-8275 UR - https://www.unboundmedicine.com/medline/citation/19602982/Artificial_neural_network_versus_multiple_logistic_function_to_predict_25_year_coronary_heart_disease_mortality_in_the_Seven_Countries_Study_ L2 - http://ovidsp.ovid.com/ovidweb.cgi?T=JS&amp;PAGE=linkout&amp;SEARCH=19602982.ui DB - PRIME DP - Unbound Medicine ER -