A review and comparison of classification algorithms for medical decision making.
Within a health care setting, it is often desirable from both clinical and operational perspective to capture the uncertainty and variability amongst a patient population, for example to predict individual patient outcomes, risks or resource needs. Homogeneity brings the benefits of increased certainty in individual patient needs and resource utilisation, thus providing an opportunity for both improved clinical diagnosis and more efficient planning and management of health care resources. A number of classification algorithms are considered and evaluated for their relative performances and practical usefulness on different types of health care datasets. The algorithms are evaluated using four criteria: accuracy, computational time, comprehensibility of the results and ease of use of the algorithm to relatively statistically naive medical users. The research has shown that there is not necessarily a single best classification tool, but instead the best performing algorithm will depend on the features of the dataset to be analysed, with particular emphasis on health care data, which are discussed in the paper.
Harper
Paul R
PR
School of Mathematics, University of Southampton, SO17 1BJ, Southampton, UK. p.r.harper@maths.soton.ac.uk
D000465:Algorithms; D003627:Data Interpretation, Statistical; D020000:Decision Support Systems, Clinical; D003663:Decision Trees; D006801:Humans; D015233:Models, Statistical; D020380:Needs Assessment; D016571:Neural Networks, Computer; D010361:Patients / Q000145:classification*; D012044:Regression Analysis; D018570:Risk Assessment; D013997:Time Factors