Trend Analysis in Stulong Data

1. Trend Analysis in Stulong Data Jiří Kléma, Lenka Nováková,Filip Karel, Olga Štěpánková The Gerstner laboratory for intelligent decision making and control Department of Cybernetics, Czech Technical University, Prague PKDD 2004, Discovery Challenge

2. Outline • Previous CTU entry • subgroup discovery (ENTRY), general CVD model • trend analysis: global approach vs. windowing • Role of windowing in mining trends • KM, Cox models in medicine • (symbolic) temporal trends in data mining • Development of windowing approach • temporal CVD definition • role of the window length • multi-feature interactions • Ordinal association rules • processing of the windowed features

3. STULONG Data • Four tables: Entry, Control, Letter, Death • Dependent variable: (static) CVD • CardioVascular Diseases • Boolean attribute derived of A2 questionnaire (Control table) CVD = false The patient has no coronary disease. CVD = true The patient has one of these attributes true (Hodn1, Hodn2, Hodn3, Hodn11, Hodn13, Hodn14) positive angina pectoris (silent) myocardial infarction ischemic heart disease cerebrovascular accident We remove patients who have diabetes (Hodn4) or cancer (Hodn15) only.

4. ENTRY - subgroup discovery • AQ no.6: Are there any differences in the ENTRY examination for different CVD groups? • Statistica 6.0 • module for interactive decision tree induction • two tailed t-test or chi-square test to asses significance of subgroups • Dependencies are relatively weak • Interesting dependencies found • social characteristics: derived attribute AGE_of_ENTRY • alcohol: “positive effect” of beer, no effect of wine • sugar consumption increases CVD risk • well-known dependencies are not mentioned (smoking, BMI, cholesterol)

5. ENTRY - general model • General CVD model (in WEKA) • feature selection + modeling (e.g., decision trees) • tends to generate trivial models (always predicting false) • asymmetric error-cost matrix does not help • Predict CVDrisk • Identify principal variables (Chi-squared test) • Naïve Bayes + ROC evaluation • three independent variables • discretized AGE_of_ENTRY • discretized BMI • Cholrisk - derived of CHLST • AUC = 0.66

6. CONTROL - trend analysis • AQ no.7: Are there any differences in development of risk factors for different CVD groups? • increasing BMI makes a contribution to CVD appearance ENTRY table CONTR table ICO – primary key Year of birth Year of entry Smoking Alcohol Cholesterol Body Mass Index Blood pressure ICO Risk factors followed during 20 years

7. Motivation • focus on development – trend gradients • possibilities • contemporary statistical methods used in medicine • KM, Cox models – analyze sth else than we want • ANOVA etc. – features have to be developed anyway, lack of data • complex sequential data mining • introduction of structural patterns and then e.g., association rules • interesting but again needs more data • our approach • introduction of simple aggregates • application of windowing • statistical evaluation for simple dependencies • ordinal association rules for more complex relations

8. Survival curves • Kaplan-Meier or Cox method • typical example of temporal analysis in medicine • regards survival period, BUT disregards development of RFs • typical scenario • distinguish groups of patients (ENTRY table) • follow their “survival” periods (DEATH or CONTROL table)

10. Intercept Correlation coefficient y (observed variable) Mean Gradient Standard deviation x (decimal time ~ year + 1/12 month) referential time (1975) Derived trend attributes

11. ICO Entry Contr1 Contr2 ContrM Aggr1 AggrN ... ... Global Approach • Risk factors to be observed are selected • SYST, DIAST, TRIGL, BMI, CHLSTMG • Selected control examinations are transformed • pivoting • Patients with no control entries are removed • about 60 patients • Trend aggregates are calculated ICO_1 ICO_2

12. Windowing Approach • Constant number of examinations for  individuals • Issues: • window length • time period vs. number of checkups • how many checkups to select? 5, 8, 10 tested • single distinct window or sliding window? • entry is used as the first examination • more records per patient  records are not independent • temporal CVD definition • CVDi - time from the last examination to CVD • yes/no (yes = CVD in the next year or CVD in future) • missing values treatment

13. Windowing – missing values approach 1: shift the series approach 2: introduce a new value

14. Window length selection

15. Window length effects • 3 different lengths tested, 5 risk factors considered • compared with the global approach • test used, • null hypothesis: independence of trends and CVD • p-values are shown • windowing: CVD1 vs. nonCVD group • global: CVD vs. nonCVD group prefer shorter windows down-up effect prefers longer windows only long term changes may have effect global approach is completely misleading

16. ControlCount vs. CVD • ControlCount • number of examinations • strong relation with CVD • AUC = 0.35 • ControlCount  CVD risk  • anachronistic attribute • introduced by the design of the study • ControlCount has influence on the trend aggregates - ControlCount  gradients tend to be more steep etc. • Conclusion: global approach cannot be applied (at least with the selected aggregates)

17. Influence of SYSTGrad (W5) • 122 individual CVD1 observations in total • SYSTGrad (W5) equi-depth binned in 5 groups • representation CVD1 group significantly increases with increasing group number of SYSTGrad

18. Averaged blood pressure • striking difference in CVD1 and nonCVD groups • linear vs. down-up development • can also be observed for the individuals – see the next slide • cannot be distinguished by longer windows

21. Averaged body mass index • difference in CVD1 and nonCVD groups • steady BMI in the nonCVD group • increasing BMI in the CVD1 group • longer windows express this trend better • this graph shows that W10 may benefit from increase between examination 9 and 8

22. smoking habits effect 1 patient state cause CVD onset effect 2 Trend factors – hypothesis testing • Influence of trend aggregates on CVD • 9 gradients considered: SYST,DIAST, CHLSTMG, TRIGLMG, BMI, HDL, LDL, POCCIG and MOC • Identified relations • decreasing HDL cholesterol level relates to the increasing risk ofCVD (p=0.001) • decreasing POCCIG (the average number of cigarettes smoked per day) relates to the increasing risk of CVD (p=0.0001) • Again: correlation vs. causality • statement 1 makes sense: HDL is a ’good’ cholesterol • statement 2 suggests spurious dependency

23. Overview of AR found • Group a – relations among trend factors • a great prevalence of the rules joining together either blood pressures (DIASTGrad and SYSTGrad) or cholesterol attributes (HLDGrad, LDLGrad and CHLSTGrad) • Group b - hypothesis to be verified by experts • insufficient target groups, 6% transactions makes 26 individuals, i.e., instead of 10 prospective diseased patients we actually observe 19

24. Conclusions • The main scope • AQ no.7: Are there any differences in development of risk factors for different CVD groups? • Contributions • Pitfalls of the global approach revealed • Windowing enabling multivariate temporal analysis proposed, effects of various window lengths studied • Development of the following risk factors may influence future CVD occurrence: • DIAST, SYST, BMI, (HDL) cholesterol, (POCCICG) • Other trends may have or intensify their influence under specific conditions (BMI trend and overweight, etc.) – we lack data to prove it