An Early Detection Method of Type-2 Diabetes Mellitus in Public Hospital

Bayu Adhi Tama, Rodiyatul F. S., Hermansyah Hermansyah


Diabetes is a chronic disease and major problem of morbidity and mortality in developing countries. The International Diabetes Federation estimates that 285 million people around the world have diabetes. This total is expected to rise to 438 million within 20 years. Type-2 diabetes mellitus (T2DM) is the most common type of diabetes and accounts for 90-95% of all diabetes. Detection of T2DM from various factors or symptoms became an issue which was not free from false presumptions accompanied by unpredictable effects. According to this context, data mining and machine learning could be used as an alternative way help us in knowledge discovery from data. We applied several learning methods, such as instance based learners, naive bayes, decision tree, support vector machines, and boosted algorithm acquire information from historical data of patient’s medical records of Mohammad Hoesin public hospital in Southern Sumatera. Rules are extracted from Decision tree to offer decision-making support through early detection of T2DM for clinicians. 

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International Diabetes Federation (IDF), What is diabetes?, World Health Organisation, accessed January 2010,

Zang Ping, et al. Economic Impact of Diabetes, International Diabetes Federation, accessed January 2010, 20impact%20of%20Diabetes.pdf.

Holt, Richard I. G., et al, editors. Textbook of Diabetes. 4th ed., West Sussex: Wiley-Blackwell; 2010.

National Diabetes Information Clearinghouse (NDIC), The Diabetes Control and Complications Trial and Follow-up Study, accessed January 2010,

N. Lavrac, E. Keravnou, and B. Zupan, Intelligent Data Analysis in Medicine, in Encyclopedia of Computer Science and Technology, vol.42, New York: Dekker, 2000.

Olson, David L and Dursun Dulen. Advanced Data Mining Techniques, Berlin: Springer Verlag, 2008.

Huang, Y., et al. Feature Selection and Classification Model Construction on Type 2 Diabetic Patients’ Data. Journal of Artificial Intelligence in Medicine, 2007; 41: 251-262.

Barakat, et al. Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus. IEEE Transactions on Information Technology in BioMedicine, 2009.

Polat, Kemal and Salih Gunes. An Expert System Approach Based on Principal Component Analysis and Adaptive Neuro-Fuzzy Inference System to Diagnosis of Diabetes Disease. Expert System with Applications, Elsevier, 2007: 702-710.

Yue, et al. An Intelligent Diagnosis to Type 2 Diabetes Based on QPSO Algorithm and WLSSVM. International Symposium on Intelligent Information Technology Application Workshops, IEEE Computer Society, 2008.

Vapnik, V. The Nature of Statistical Learning Theory 2nd Edition, New York: Springer Verlag, 2000.

Witten, I.H., Frank, E. Data mining: Practical Machine Learning Tools and Techniques 2nd Edition. San Fransisco: Morgan Kaufmann. 2005.

Alpaydm, Ethem. Introduction to Machine Learning, Massachusetts: MIT Press, 2004: 154-155.

Han, J. and Micheline Kamber. Data Mining: Concepts and Techniques, San Fransisco: Morgan Kaufmann Publisher, 2006: 310-311.

Kohavi, R., Scaling Up the Accuracy of Naive Bayes Classifiers: A Decision Tree Hybrid, Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 1996.

Freund, Y., Schapire, R.E. Experiments with a New Boosting Algorithm. Proceedings of the Thirteenth International Conference on Machine Learning. San Francisco: Morgan Kaufmann, 1996: 148–156.

Opitz, D., Maclin, R.: Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research,1999, 11: 169–198.

Fawcett, Tom. An Introduction to ROC Analysis. Pattern Recognition Letters, Elsevier, 2006; 27: 861-874.

Zou, Kelly H. ROC literature research, On-line bibiography accessed February 2011,



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