COMPARING ASSOCIATION RULES AND DECISION TREES FOR DISEASE PREDICTIONCarlos Ordonez

MOTIVATION

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

MOTIVATION

Decision tree a well-known machine learning algorithm

Association rules vs. Decision tree Accuracy Interpretability Applicability

ASSOCIATION RULES

Support Confidence Lift

Lift quantifies the predictive power of x y Rules such that lift(xy) > 1 are interesting!

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CONSTRAINED ASSOCIATION RULES

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

CONSTRAINED ASSOCIATION RULES

Search Constraints1. Max itemset size (k)

Reduces the combinatorial explosion of large itemsets and helps finding simple rules

2. Group gi >0 Aj belongs to a group

gi =0 Aj is not group-constrained at all This avoids finding trivial or redundant rules

3. Antecedent/Consequentci = 1 Ai is an antecedent

ci = 2 Ai is a consequent

Patients 655

attributes 25

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

PARAMETERS

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.

HEALTHY ARTERIES

9,595 associations 771 rules

DISEASED ARTERIES

Several unneeded itemswere filtered out ( with values in lower (healthy)ranges)

10,218 associations 552 rules

PREDICTIVE RULES FROM DECISION TREES

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

PREDICTIVE RULES FROM DECISION TREES

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

PREDICTIVE RULES FROM DECISION TREES

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

RELATED WORK

PREDICTIVE RULES FROM DECISION TREES

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

DISCUSSION

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

DISCUSSION - 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)

DISCUSSION – DECISION TREE ADVANTAGES

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