Machine Learning Approaches to Medical Decision MakingKonstantinos Veropoulos, Machine Learning Approaches to Medical Decision Making. PhD thesis. Department of Computer Science, University of Bristol. March 2001. PDF, 6231 Kbytes.
Recent advances in computing and developments in technology have facilitated the rou-tine collection and storage of medical data that can be used to support medical decisions. In most cases however, there is a need for the collected data to be analysed in order for a medical decision to be drawn, whether this involves diagnosis, prediction, course of treat-ment, or signal and image analysis. Intelligent machine learning methods such as neural computing and support vector machines can be shown to be suitable approaches to such complex tasks. This thesis presents a study of the use of intelligent methods for medical decision making that aims to investigate and demonstrate their potential in such an application. The medical application presented in this work is the automated identification of tubercle bacilli (bacteria responsible for the tuberculosis disease) from photomicrographs of spu-tum smears. The development of such a system is of great importance since ---according to the World Health Organization--- tuberculosis kills more adults than any other infectious disease and its now on the increase. The diagnosis of tuberculosis is based on the screening of sputum smears under a microscope and therefore involves a medical image analysis task. Artificial neural networks and support vector machines have been employed for the detection and identification of tubercle bacilli and are supported by conventional image processing techniques. The basic concepts and algorithms underlying these methods are described and analysed in the first half of this documentation. The investigation of the main medical imaging and classifica-tion task that follows is carried out in two stages. First a preliminary study is described showing an overall generalization performance of up to 92.1% (achieving up to 93.5% sensitivity and 94.5% selectivity between different classification methods) on a small set of 1147 pattern examples from five different patients. This preliminary investigation has been used to support the fact that the development of a reliable and accurate classification system is feasible. A second study used larger data sets constructed from 65 Auramine-stained smears. This investigation showed a slightly lower generalisation performance that reached an overall accuracy of 87.6% (achieving up to 93.9% sensitivity and 86.9% specificity between different classification methods). This second study involved several different data sets based on the medical preparation of the slides. Sputum smears used for identifying tubercle bacilli can be prepared using either Auramine/Rhodamine stain or a Ziehl-Neelsen stain yielding monochrome (fluo-rescent) or colour images. This presents an additional complexity to the image analysis and classification task, since both these modalities have to be taken into consideration. Although the set of smears and images used in the main investigation need to be much larger in order