Abstract
Diabetes is a metabolic disorder which can be greatly affected by lifestyle. The disease cannot be cured but can be controlled, which will minimize the complications such as heart disease, stroke and blindness. Clinicians routinely collect large amounts of information on diabetic patients as part of their day to day management for control of the disease. We investigate the potential for data mining in order to spot trends in the data and attempt to predict outcome. Feature selection has been used to improve the efficiency of the data mining algorithms and identify the contribution of different features to diabetes control status prediction. Decision trees can provide classification accuracy over 78%. However, while most bad control cases (90%) can be correctly classified, at least 50% of good control cases will be misclassified, which means that current feature selection and prediction models illustrate some potential but need additional refinement.
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Huang, Y., McCullagh, P., Black, N., Harper, R. (2004). Evaluation of Outcome Prediction for a Clinical Diabetes Database. In: López, J.A., Benfenati, E., Dubitzky, W. (eds) Knowledge Exploration in Life Science Informatics. KELSI 2004. Lecture Notes in Computer Science(), vol 3303. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30478-4_16
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DOI: https://doi.org/10.1007/978-3-540-30478-4_16
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