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Activity Number:
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526
- Contributed Poster Presentations: Section on Statistical Consulting
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Type:
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Contributed
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Date/Time:
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Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Consulting
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Abstract #304766
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Title:
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Using Linear Discriminant Analysis to Classify Patients with Variant Von Willebrand Disease (VWD)
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Author(s):
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Ke Yan* and Jonathan C Roberts and Robert Montgomery and Pamela Christopherson and Pippa Simpson
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Companies:
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Medical College of Wisconsin and Bleeding & Clotting Disorders Institute and Blood Center of Wisconsin and Blood Center of Wisconsin and Medical College of Wisconsin
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Keywords:
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Multiclass classification ;
Linear discriminant analysis
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Abstract:
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Introduction: 134 patients with VWD, a common inherited bleeding disorder, were evaluated. There are different types of VWD depending on the VWF activity defect, and a novel assay to discriminate the various activities of VWF was developed. Of interest was whether this assay was useful in correctly classifying different variants of VWD. Methods: Classification tree analyses can be very tedious and difficult to interpret using one-vs-the rest or all-vs-all others algorithm. Multinomial logistic regression analyses results were unstable because of the quasi-complete separation problem. Linear discriminant analysis (LDA) was ultimately used with centered quadratic terms to account for non-linearity and multicollinearity in the data. The classification results using the study assay were compared to the actual clinical diagnoses. And the final model was cross-validated using the leave-one-out algorithm. Results: We were able to classify patients with an overall accuracy of 93.3% (125 out of 134). When using the leave-one-out algorithm, 118 out of 134 (88.1%) patients were correctly classified. Conclusion: LDA can be very useful in dealing with multiclass classifications.
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Authors who are presenting talks have a * after their name.
