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Disease Diagnosis: Optimization-Based Methods

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Encyclopedia of Optimization

Introduction

Classification is a fundamental machine learning task whereby rules are developed for the allocation of independent observations to groups. Classic examples of applications include medical diagnosis – the allocation of patients to disease classes on the basis of symptoms and laboratory tests – and credit screening – the acceptance or rejection of credit applications on the basis of applicant data. Data are collected concerning observations with known group membership. These training data are used to develop rules for the classification of future observations with unknown group membership.

In this introduction, we briefly describe some terminologies related to classification, and provide a brief description of the organization of this chapter.

Pattern Recognition, Discriminant Analysis, and Statistical Pattern Classification

Cognitive science is the science of learning, knowing, and reasoning. Pattern recognitionis a broad field within...

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Authors and Affiliations

  1. Center for Operations Research in Medicine and HealthCare, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, USA

    Eva K. Lee & Tsung-Lin Wu

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  1. Eva K. Lee
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Editors and Affiliations

  1. Department of Chemical Engineering, Princeton University, Princeton, NJ, 08544-5263, USA

    Christodoulos A. Floudas

  2. Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, 32611-6595, USA

    Panos M. Pardalos

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K. Lee, E., Wu, TL. (2008). Disease Diagnosis: Optimization-Based Methods . In: Floudas, C., Pardalos, P. (eds) Encyclopedia of Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74759-0_133

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