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A General-Purpose Multi-stage Multi-group Machine Learning Framework for Knowledge Discovery and Decision Support

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1842))

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Abstract

We present a general-purpose multi-stage, multi-group machine learning framework that incorporates the discriminant analysis via mixed integer programming (DAMIP) classifier with an exact combinatorial branch-and-bound (BB) algorithm and a fast particle swarm optimization (PSO) for feature selection. DAMIP delays making decisions on ‘difficult-to-classify’ observations by placing them into a reserved judgment region and develops new classification rules in a later stage. Such a design is well-suited for poorly separated data that are difficult to classify without committing a high percentage of misclassification errors. The model misclassification limits, and reserved judgment levels can be fine-tuned to facilitate the efficient management of imbalanced groups. This ensures that the minority groups (with relatively few entities) are treated equally as the majority groups. We tackle four medical problems that involve poorly separated data and imbalanced groups in which traditional classifiers yield low prediction accuracy: (a) multi-site treatment outcome prediction for best practice discovery in cardiovascular disease; and (b) diabetes; (c) early disease diagnosis in predicting subjects into normal cognition, mild cognitive impairment, and Alzheimer’s disease groups using neuropsychological tests and blood plasma biomarkers; and (d) uncovering patient characteristics that predict optimal response to intra-articular injections of hyaluronic acid for knee osteoarthritis. The multi-stage BB-PSO/DAMIP returns interpretable predictive results with over 80% blind prediction accuracy. One advantage of our findings is that the features identified are easily interpreted and understood by clinicians as well as patients. All of which can have a significant impact on translating the findings to clinical practice to achieve an improved quality of life and medical outcome. The multiple rules with relatively small subsets of discriminatory features afford flexibility for different sites (and different patient populations) to adopt different policies for implementing the best practice.

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Acknowledgements

A portion of the results from this project (the machine learning advances, and the results obtained for cardiovascular disease and diabetes) received the first runner-up prize at the 2019 Caterpillar and INFORMS Innovative Applications in Analytics Award. This work is partially supported by grants from the National Science Foundation (IIP-1361532), and the American Orthopedic Society for Sports Medicine. Findings and conclusions in this paper are those of the authors and do not necessarily reflect the views of the National Science Foundation and the American Orthopedic Society for Sports Medicine. The authors would like to acknowledge the participation of Zhuonan Li in this project. The authors also thank Dr. Allan Levey, Dr. Felicia Goldstein, and Dr. William Hu of Emory Alzheimer’s Disease Research Center for their collaboration and clinical advice. The authors extend their deepest respect and gratitude to the late Dr. Barton J. Mann PhD, with whom we collaborated on the knee osteoarthritis research, and to Dr. Captain Marlene DeMaio for her clinical guidance and collaboration on the project. We thank the anonymous reviewers for their useful comments.

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Authors and Affiliations

  1. Center for Operations Research in Medicine and Healthcare, The Data and Analytics Innovation Institute, Atlanta, USA

    Eva K. Lee

  2. Georgia Institute of Technology, Atlanta, USA

    Eva K. Lee & Fan Yuan

  3. The American Orthopedic Society for Sports Medicine, Chicago, USA

    Barton J. Man

  4. American Medical Association, Chicago, USA

    Brent Egan

  5. Medical University of South Carolina, Charleston, USA

    Brent Egan

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  1. Eva K. Lee
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Correspondence to Eva K. Lee .

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Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Frans Coenen

  2. Instituto de Telecomunicações and University of Lisbon, Lisbon, Portugal

    Ana Fred

  3. Universidade da Madeira and Madeira-ITI, Funchal, Portugal

    David Aveiro

  4. Delft University of Technology, Delft, The Netherlands

    Jan Dietz

  5. Polytechnic Institute of Coimbra - ISEC, Coimbra, Portugal

    Jorge Bernardino

  6. University of Naples Federico II, Naples, Italy

    Elio Masciari

  7. Polytechnic Institute of Setúbal and INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Lee, E.K., Yuan, F., Man, B.J., Egan, B. (2023). A General-Purpose Multi-stage Multi-group Machine Learning Framework for Knowledge Discovery and Decision Support. In: Coenen, F., et al. Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2022. Communications in Computer and Information Science, vol 1842. Springer, Cham. https://doi.org/10.1007/978-3-031-43471-6_4

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