A Classification Algorithm that Derives Weighted Sum Scores for Insight into Disease

Quinn, A., Stranieri, A., Yearwood, J.L. and Hafen, G.

Data mining is often performed with datasets associated with diseases in order to increase insights that can ultimately lead to improved prevention or treatment. Classification algorithms can achieve high levels of predictive accuracy but have limited application for facilitating the insight that leads to deeper understanding of aspects of the disease. This is because the representation of knowledge that arises from classification algorithms is too opaque, too complex or too sparse to facilitate insight. Clustering, association and visualisation approaches enable greater scope for clinicians to be engaged in a way that leads to insight, however predictive accuracy is compromised or non-existent. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classification algorithm that provides accuracy comparable to other techniques whilst providing some insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. Clinicians are very familiar with weighted sum scoring scales so the internal representation is intuitive and easily understood. This paper presents results from the use of the AWSum approach with data from patients suffering from Cystic Fibrosis.

Cite as: Quinn, A., Stranieri, A., Yearwood, J.L. and Hafen, G. (2009). A Classification Algorithm that Derives Weighted Sum Scores for Insight into Disease. In Proc. Third Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2009), Wellington, New Zealand. CRPIT, 97. Warren, J. R., Ed. ACS. 13-17.

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