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Machine Learning for Designing an Automated Medical Diagnostic System

Machine Learning for Designing an Automated Medical Diagnostic System
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Author(s): Ahsan H. Khandoker (The University of Melbourne, Australia) and Rezaul K. Begg (Victoria University, Australia)
Copyright: 2009
Pages: 16
Source title: Handbook of Research on Modern Systems Analysis and Design Technologies and Applications
Source Author(s)/Editor(s): Mahbubur Rahman Syed (Minnesota State University Mankato, USA) and Sharifun Nessa Syed (Minnesota State University - Mankato, USA)
DOI: 10.4018/978-1-59904-887-1.ch030

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Abstract

This chapter describes the application of machine learning techniques to solve biomedical problems in a variety of clinical domains. First, the concept of development and the main elements of a basic machine learning system for medical diagnostics are presented. This is followed by an introduction to the design of a diagnostic model for the identification of balance impairments in the elderly using human gait pattern, as well as a diagnostic model for predicating sleep apnoea syndrome from electrocardiogram recordings. Examples are presented using support vector machines (a machine learning technique) to build a reliable model that utilizes key indices of physiological measurements (gait/electrocardiography [ECG] signals). A number of recommendations have been proposed for choosing the right classifier model in designing a successful medical diagnostic system. The chapter concludes with a discussion of the importance of signal processing techniques and other future trends in enhancing the performance of a diagnostic system.

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