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Coronary Risk Prediction by Logical Analysis of Data

Author

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  • Sorin Alexe
  • Eugene Blackstone
  • Peter Hammer
  • Hemant Ishwaran
  • Michael Lauer
  • Claire Pothier Snader

Abstract

The objective of this study was to distinguish within a population of patients with known or suspected coronary artery disease groups at high and at low mortality rates. The study was based on Cleveland Clinic Foundation's dataset of 9454 patients, of whom 312 died during an observation period of 9 years. The Logical Analysis of Data method was adapted to handle the disproportioned size of the two groups of patients, and the inseparable character of this dataset – characteristic to many medical problems. As a result of the study, we have identified a high-risk group of patients representing 1/5 of the population, with a mortality rate 4 times higher than the average, and including 3/4 of the patients who died. The low-risk group identified in the study, representing approximately 4/5 of the population, had a mortality rate 3 times lower than the average. A Prognostic Index derived from the LAD model is shown to have a 83.95% correlation with the mortality rate of patients. The classification given by the Prognostic Index was also shown to agree in 3 out of 4 cases with that of the Cox Score, widely used by cardiologists, and to outperform it slightly, but consistently. An example of a highly reliable risk stratification system using both indicators is provided. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • Sorin Alexe & Eugene Blackstone & Peter Hammer & Hemant Ishwaran & Michael Lauer & Claire Pothier Snader, 2003. "Coronary Risk Prediction by Logical Analysis of Data," Annals of Operations Research, Springer, vol. 119(1), pages 15-42, March.
    • Handle: RePEc:spr:annopr:v:119:y:2003:i:1:p:15-42:10.1023/a:1022970120229
    DOI: 10.1023/A:1022970120229
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    Citations

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    Cited by:

    1. Lejeune, Miguel & Lozin, Vadim & Lozina, Irina & Ragab, Ahmed & Yacout, Soumaya, 2019. "Recent advances in the theory and practice of Logical Analysis of Data," European Journal of Operational Research, Elsevier, vol. 275(1), pages 1-15.
    2. Pierre Lemaire, 2011. "Extensions of Logical Analysis of Data for growth hormone deficiency diagnoses," Annals of Operations Research, Springer, vol. 186(1), pages 199-211, June.
    3. Chun-An Chou & Tibérius O. Bonates & Chungmok Lee & Wanpracha Art Chaovalitwongse, 2017. "Multi-pattern generation framework for logical analysis of data," Annals of Operations Research, Springer, vol. 249(1), pages 329-349, February.
    4. Ahmed Ragab & Mohamed-Salah Ouali & Soumaya Yacout & Hany Osman, 2016. "Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan–Meier estimation," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 943-958, October.
    5. Jocelyn, Sabrina & Chinniah, Yuvin & Ouali, Mohamed-Salah & Yacout, Soumaya, 2017. "Application of logical analysis of data to machinery-related accident prevention based on scarce data," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 223-236.
    6. Kedong Yan & Hong Seo Ryoo, 2019. "A multi-term, polyhedral relaxation of a 0–1 multilinear function for Boolean logical pattern generation," Journal of Global Optimization, Springer, vol. 74(4), pages 705-735, August.
    7. Talayeh Razzaghi & Ilya Safro & Joseph Ewing & Ehsan Sadrfaridpour & John D. Scott, 2019. "Predictive models for bariatric surgery risks with imbalanced medical datasets," Annals of Operations Research, Springer, vol. 280(1), pages 1-18, September.
    8. Guo, Cui & Ryoo, Hong Seo, 2021. "On Pareto-Optimal Boolean Logical Patterns for Numerical Data," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    9. Janostik, Radek & Konecny, Jan & Krajča, Petr, 2020. "Interface between Logical Analysis of Data and Formal Concept Analysis," European Journal of Operational Research, Elsevier, vol. 284(2), pages 792-800.
    10. Kedong Yan & Dongjing Miao & Cui Guo & Chanying Huang, 2021. "Efficient feature selection for logical analysis of large-scale multi-class datasets," Journal of Combinatorial Optimization, Springer, vol. 42(1), pages 1-23, July.
    11. Travaughn C. Bain & Juan F. Avila-Herrera & Ersoy Subasi & Munevver Mine Subasi, 2020. "Logical analysis of multiclass data with relaxed patterns," Annals of Operations Research, Springer, vol. 287(1), pages 11-35, April.
    12. Maurizio Maravalle & Federica Ricca & Bruno Simeone & Vincenzo Spinelli, 2015. "Carpal Tunnel Syndrome automatic classification: electromyography vs. ultrasound imaging," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 100-123, April.
    13. Gabriela Alexe & Sorin Alexe & Peter Hammer & Bela Vizvari, 2006. "Pattern-based feature selection in genomics and proteomics," Annals of Operations Research, Springer, vol. 148(1), pages 189-201, November.
    14. Kedong Yan & Hong Seo Ryoo, 2017. "Strong valid inequalities for Boolean logical pattern generation," Journal of Global Optimization, Springer, vol. 69(1), pages 183-230, September.

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