The document discusses using modified α-trees and disjunctive rule combinations to segment diabetes risk groups from a large healthcare dataset. It aims to extract simple, interpretable rules in natural language to identify high-risk groups with diabetes rates twice the normal population as well as low-risk groups with rates four times lower. The method is demonstrated on the BRFSS dataset, extracting rules to define different risk segments based on attributes like blood pressure, health ratings, vaccinations and more. The approach provides alternatives to traditional machine learning for imbalanced healthcare classification problems.

1. LOW/HIGH-RISK DIABETES GROUP SEGMENTATION USING α-TREESAMA-IEEE Medical Technology Conference 2011Anurekha Ramakrishnan1, Yubin Park2, Joydeep Ghosh21Dept. of Statistics and Scientific Computation2Dept. of Electrical and Computer EngineeringThe University of Texas at Austin

2. Barriers to M/C learning Adoption in HealthcareClass-imbalance Target ratios are often extremely skewed.Mismatch with Performance Metrics ‘Misclassification rates may not be relevantAsymmetric costs involved. ‘Sensitivity/Specificity’ or ‘Lift’ should be a part of learning goals.Interpretation of ResultsSimple AND/OR Rules (in Natural Language) are desirable.We suggest a possible solution for these problems using:Modified α-Trees,Disjunctive Combination of Rules.

3. ObjectivesOther Requirements:Interpretable segmentation - AND, OR Rules in Natural languageExtensive coverage using Simple rules.Note: These objectives are different from traditional machine learning objectives. The objectives are based on the observations on many failed Medical Decision Support systems.

4. BRFSS DatasetBehavioral Risk Factor Surveillance SystemURL: http://www.cdc.gov/brfss/The largest telephone survey since 1984.Tracks health conditions and risk behaviors in the United States.Contains information on a variety of diseasese.g. diabetes, hypertension, cancer, asthma, HIV, etc. More than 400,000 records per year.Many states use BRFSS data to support health-related legislative efforts.

5. α-Tree1A Decision Tree Algorithm (e.g. CART, C4.5)Decision criterion: α-Divergence.Generalizes C4.5.Robust performance in class-imbalance settings.Stop its growth when a Low/High-risk group is obtained. (modified α-Tree)Different ‘α’ values result in different decision rules.Decision trees provide greedy solutions (sub-optimal solutions).By disjunctively combining different solutions from different α-Trees, we can approach to a better solution.Python Code available (http://www.ideal.ece.utexas.edu/~yubin/)1. Y.Park and J.Ghosh, “Compact Ensemble Trees for Imbalanced Data,” in 10th International Wokshop on Multiple Classifier Systems, Italy, June 2011.

6. 3-Phase DiagramExample)When High-risk group is defined as more than 24% Diabetes Rate group. - Twice Higher rate than Normal Population Rule1:RFHYPE5 = 1 & AGE_G >= 5.0 & RFHLTH = 2 & BMI4CAT >= 2.0 from α=0.1 ORRule 2: RFHYPE5 ≠ 1 & RFHLTH = 1 & BMI4CAT >= 2.9 & PNEUVAC3 = 1 from α=1.0 ORRule 3: RFHYPE5 = 2 & RFHLTH ≠ 1from α=1.5 OR … These combined rules extract High-risk Diabetes Segments (>24%).

7. Example Tree StructureWhen α=2.0, total five High-risk Segmentation Rules are extracted.Different α values result in different tree structures.YesNo

8. Results for Twice Higher Diabetes Rate Group (High-risk)Resultant Rules from α-Trees.RFHYPE5 = 2 & RFHLTH ≠1RFHYPE5 ≠2 & RFHLTH = 2 & RFCHOL = 2…English TranslationSegment 1: They have high-blood pressure and think themselves unhealthy (including not responding to this question).Segment 2: They have high cholesterol and think themselves unhealthy. But they don’t have high-blood pressure.…

9. Results for Four-times lower Diabetes Rate Group (Low-risk)Resultant Rules from α-Trees.RFHYPE5 ≠2 and RFHLTH ≠2 and PNEUVAC3 ≠1 RFHYPE5 =1 and RFHLTH ≠2 and AGE_G < 5.0…English TranslationSegment 1: They don’t have high blood pressure and think themselves healthy. They had a pneumonia shot at least once in their life time.Segment 2: They have high blood pressure, but think themselves healthy and are under 50 yrs of age.…

10. AppendixAα-DivergenceSpecial cases

11. AppendixBModified α-Tree AlgorithmInput: BRFSS (input data), α (parameter)Output: Low-risk group extraction rulesSelect the best feature, which gives the maximum α-divergence criterion.If (no such feature) or (number of data points < cut-off size) or (This group is a low/high-risk group) then stop its growth.ElseSegment the input data based on the best feature.Recursively run Modified α-Tree Algorithm( segmented data, α)