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STULONG Discovery Challenges Feedback

STULONG Discovery Challenges Feedback. Marie Tomečková EuroMISE – Cardio This work is supported by the project LN00B107 of the Ministry of Education of the Czech Republic. STULONG Challenges – Medical feedback STULONG = acronym LONG itudinal STU dy. Main aims of the study:

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STULONG Discovery Challenges Feedback

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  1. STULONG Discovery Challenges Feedback Marie Tomečková EuroMISE – Cardio This work is supported by the project LN00B107 of the Ministry of Education of the Czech Republic

  2. STULONG Challenges – Medical feedbackSTULONG = acronymLONGitudinal STUdy Main aims of the study: To determine prevalence of the risk factors of atherosclerosis in middle-age men • To follow up the development of the risk factors • To asses the possibilities and the influence of the complex intervention on the incidence and values of the risk factors and on the cardiovascular mortality

  3. Atherosclerosis: • a total complicated disease of all over organism • a dynamic process, it begins in childhood and adolescence and continues for the whole life • opinions on the origin and progress of the disease are developing • interaction and influence of genetic predisposition and exterior environment • the influence of so-called risk factors is still regarded • some so-called protective factors exist

  4. STULONG - analysis • Statistical - descriptive statistics - logistic regression - survival analysis • Data mining - different methods - resulting in different conclusions

  5. Prevalence of risk factors in RG

  6. Mortality caused by atherosclerotic CVD

  7. Mortality caused by atherosclerotic CVD depending on the number of RFA

  8. Kaplan-Meier analysisin RG (at age of 38-44years)depending on the number of RFA

  9. Kaplan-Meier analysisin RG (at age of 45-53years)depending on the number of RFA

  10. The relative risk of death caused by other. CVD

  11. Different approaches to solve the analytic questions • univaried and bivaried data analysis • association rules • trend analysis • analysis so called time windows • ROC analysis • Miner tool SDS, WEKA tool, STATISTICA tool • genetic approach • standard attribute-value data mining techniques • inductive logic programming technique

  12. Different approaches to solve the analytic questions – continue: • fuzzy approximate dependencies • explicit relations - functional dependencies • the inductive logic programming technique • Rough Set Exploration System to solve both classification and descriptive tasks • approach to generate a mathematical algebraic model – discriminate function - Werner, Kalganova • the selection of = interesting = emerging patterns (strong emerging patterns

  13. Challenge 2003 Some approaches to solve the analytic questions • Genetic approach – function based on the Area Under the ROC curve - Conclusions very good understanable Azé,J. - Lucas,N. - Sebag,M.: A New Medical Test for Atherosclerosis Detection GeNo • Fuzzy Approximate Dependencies – Fuzzy logic – Interesting relations – for discussion Berzal,F. - Cubero,J.C.- Sanchez,D. - Serrano,J.M. - Vila,M.A.: Finding Fuzzy Approximate Dependencies within STULONG Data

  14. Challenge 2003 – cont.Some approaches to solve the analytic questions • Association rules – see later (Prague) Burian,J. - Rauch,J.: Analysis of Death Causes in the STULONG Data Set • Strongest Emerging Patterns – very interesting approach, results to discuss Cremilleux,B.- Soulet,A. - Rioult,F.: Mining the Strongest Emerging Patterns Characterizing Patients Affected by Diseases Due to Atherosclerosis • Rough Set Exploration System (RSES) – experimental tool, not yet implemented, without explications Hoa,N.S. - Son,N.H.: Analysis of STULONG Data by Rough Set Exploration System (RSES)

  15. Challenge 2003 – cont.Some approaches to solve the analytic questions • SDS rules (Set Differs of Set) – some very interesting results – diferences among the groups in more than two variables – good conclusions Karban,T.: SDS-Rules and Classification on PKDD2003 Discovery Challenge • Trend analysis, analysis so called time windows- interesting approache, some conlusiones to discuss from medical point of view Novakova,L. - Klema,J. - Jakob,M.- Rawles,S. - Stepankova,O.: Trend Analysis and Risk Identification

  16. Challenge 2003 – cont.Some approaches to solve the analytic questions • WEKA tool, ACE data tool – very good presentation with ilustrative explanations Van Assche,A. - Verbaeten,S. - Krzywania,D. - Struyf,J. - Blockeel,H.: Attribute-Value and First Order Data Mining within the STULONG Project • Discriminate function Werner,J.C. - Kalganova,T.: Risk Evaluation using Evolvable Discriminate Function

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