Authors:
Eva K. Lee
1
;
2
and
Brent Egan
3
;
4
Affiliations:
1
Center for Operations Research in Medicine and Healthcare, The Data and Analytic Innovation Institute, Atlanta, U.S.A.
;
2
Georgia Institute of Technology, Atlanta, U.S.A.
;
3
American Medical Association, Chicago, U.S.A.
;
4
Medical University of South Carolina, U.S.A.
Keyword(s):
Multi-stage Classification, Multi-group Classification, Imbalanced Data, Discriminant Analysis via Mixed Integer Program, Cardiovascular Disease, Multi-Site Knowledge Discovery, Best Practice, Alzheimer’s Disease, Mild Cognitive Impairment, Early Diagnosis of Dementia, Machine Learning for Evidence-based Practice, Branch-and-Bound.
Abstract:
We present a multi-stage, multi-group classification framework that incorporates discriminant analysis via mixed integer programming (DAMIP) with an exact combinatorial branch-and-bound (BB) algorithm and a fast particle swarm optimization (PSO) for feature selection for classification. By utilizing a reserved judgment region, DAMIP allows the classifier to delay making decisions on ‘difficult-to-classify’ observations and develop new classification rules in a later stage. Such a design works well for mixed (poorly separated) data that are difficult to classify without committing a high percentage of misclassification errors. We also establish variant DAMIP models that enable problem-specific fine tuning to establish proper misclassification limits and reserved judgement levels that facilitate efficient management of imbalanced groups. This ensures that minority groups with relatively few entities are treated equally as the majority groups. We apply the framework to two real-life med
ical problems: (a) multi-site treatment outcome prediction for best practice discovery in cardiovascular disease, and (b) early disease diagnosis in predicting subjects into normal cognition, mild cognitive impairment, and Alzheimer’s disease groups using neuropsychological tests and blood plasma biomarkers. Both problems involve poorly separated data and imbalanced groups in which traditional classifiers yield low prediction accuracy. The multi-stage BB-PSO/DAMIP manages the poorly separable imbalanced data well and returns interpretable predictive results with over 80% blind prediction accuracy. Mathematically, DAMIP is NP-complete with its classifier proven to be universally strongly consistent. Hence, DAMIP has desirable solution characteristics for machine learning purposes. Computationally, DAMIP is the first multi-group, multi-stage classification model that simultaneously includes a reserved judgment capability and the ability to constrain misclassification rates within a single model. The formulation includes constraints that transform the features from their original space to the group space, serving as a dimension reduction mechanism.
(More)