C OMPARING A SSOCIATION R ULES AND D ECISION T REES FOR D ISEASE P REDICTION Carlos Ordonez

M OTIVATION Three main issues about mining association rules in medical datasets: 1. A significant fraction of association rules is irrelevant 2. Most relevant rules with high quality metrics appear only at low support 3. # of discovered rules becomes extremely large at low support Search constraints: Find only medically significant association rules Make search more efficient

M OTIVATION Decision tree  a well-known machine learning algorithm Association rules vs. Decision tree Accuracy Interpretability Applicability

A SSOCIATION R ULES Support Confidence Lift Lift quantifies the predictive power of x  y Rules such that lift(x  y) > 1 are interesting!

C ONSTRAINED A SSOCIATION R ULES Transforming Medical Data Set Data must be transformed to binary dimensions Numeric attributes  intervals, each interval is mapped to an item. Categorical attributes  each categorical value is an item If an attribute has negation add that as an item Each item is corresponds to the presence or absence of one categorical value or one numeric interval

C ONSTRAINED A SSOCIATION R ULES Search Constraints 1. Max itemset size (k) Reduces the combinatorial explosion of large itemsets and helps finding simple rules 2. Group g i >0  A j belongs to a group g i =0  A j is not group-constrained at all This avoids finding trivial or redundant rules 3. Antecedent/Consequent c i = 1  A i is an antecedent c i = 2  A i is a consequent

Patients655 attributes25 Percentage of vessel narrowing LAD, LCX and RCA are binned at 70% and 50% LM is binned at 30% and 50% 9 heart regions ( 2 ranges with 0.2 as cutoff) Binned at 40(adult) and 60(old) Binned at 200 and 250

P ARAMETERS k = 4 Min support = 1% ≈ 7 Min confidence = 70% Min lift = 1.2 To get rules where there is stronger implication dependence between X and Y Rules with conf ≥ 90 and lift ≥ 2, with 2 or more items in the consequent were considered medically significant.

H EALTHY ARTERIES 9,595 associations 771 rules

D ISEASED A RTERIES Several unneeded items were filtered out ( with values in lower (healthy) ranges) 10,218 associations 552 rules

P REDICTIVE R ULES FROM D ECISION T REES CN4.5  using gain ratio CART  similar results Threshold for the height of the tree to produce simple rules Percentage of patients (ls) Fraction of patients where the antecedent appears Confidence factor (cf) Focus on predicting LDA disease

P REDICTIVE R ULES FROM D ECISION T REES 1. All measurements without binning as independent variables, numerical variables are automatically split Without any threshold on height: 181 node 90% accuracy height = 14 most rules more than 5 attributes except 5 rules, other involve less than 2% of the patients More than 80% of rules refer to less than 1% of patients Many rules involve attributes with missing information Many rules had the same variable being split several times Few rules with cf = 1 but splits included borderline cases and involves few patients

P REDICTIVE R ULES FROM D ECISION T REES With threshold = 10 on height 83 nodes 77% accuracy Most rules have repeated attributes More than 5 attributes Perfusion cutoffs higher than 0.5 Low cf and involved less than 1% of the population With threshold = 3 on height 65% accuracy Simpler rules

R ELATED W ORK

P REDICTIVE R ULES FROM D ECISION T REES 2. Items (binary variables) as independent variables like association rules are used With threshold = 3 on height Most of the rules were much closer to the prediction requirements 10 nodes

D ISCUSSION Decision trees are not as powerful as association rules in this case Do not work well with combinations of several target variables Fail to identify many medically relevant combinations of independent numeric variable ranges and categorical values Tend to find complex and long rules, if the height is unlimited Find few predictive rules with reasonably sized (>1%) sets of patients in such cases Rules some times repeat the same attribute

D ISCUSSION - ALTERNATIVES build many decision trees with different independent attributes It’s error-prone, difficult to interpret, slow for higher # of attributes Create a family of small trees, each tree has a weight Each tree becomes similar to a small set of association rules Constraints for association rules can be adopted to decision trees (future work)

D ISCUSSION – D ECISION T REE A DVANTAGES DT partitions the data set, ARs on the same target attributes may refer to overlap DT represents a predictive model of data set, ARs are disconnected among themselves DT is guaranteed to have at least 50% prediction accuracy and generally above 80% for binary target variables, ARs require trial and error to find the best threshold