AWSum: applying data mining in a health care scenario

A. Quinn, Herbert Jelinek, A. Stranieri, J. Yearwood

Research output: Book chapter/Published conference paperConference paperpeer-review

1 Citation (Scopus)
30 Downloads (Pure)


This paper investigates the application of a new data mining algorithm called Automated Weighted Sum, (AWSum), to diabetes screening data to explore its use in providing researchers with new insight into the disease and secondarily to explore the potential the algorithm has for the generation of prognostic models for clinical use. There are many data mining classifiers that produce high levels of predictive accuracy but their application to health research and clinical applications is limited because they are complex, produce results that are difficult to interpret and are difficult to integrate with current knowledge and practises. This is because most focus on accuracy at the expense of informing the user as to the influences that lead to their classification results. By providing this information on influences a researcher can be pointed to new potentially interesting avenues for investigation. AWSum measures influence by calculating a weight for each feature value that represents its influence on a class value relative to other class values. The results produced, although on limited data, indicated the approach has potential uses for research and has some characteristics that may be useful in the future development of prognostic models.
Original languageEnglish
Title of host publicationISSNIP 2008
Place of PublicationNew Jersey
Number of pages6
ISBN (Electronic)9781424438228
Publication statusPublished - 2008
EventIEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP) - Sydney, NSW Australia, Australia
Duration: 15 Dec 200818 Dec 2008


ConferenceIEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)


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