Abstract
A number of machine learning models have been applied to a prediction or classification task of diabetes. These models either tried to categorise patients into insulin and non-insulin, or anticipate the patients’ blood surge rate. Most medical experts have realised that there is a great relationship between patient’s symptoms with some chronic diseases and the blood sugar rate. This paper proposes a diabetes-chronic disease prediction-description model in the form of two sub-modules to verify this relationship. The first sub-module uses Artificial Neural Network (ANN) to classify the types of case and to predict the rate of fasting blood sugar (FBS) of patients. The post-process module is used to figure out the relations between the FBS and symptoms with prediction rate. The second sub-module describes the impact of the rate of FBS and the symptoms on the patient’s health. Decision Trees (DT) is used to achieve the description part of diabetes symptoms.
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References
Srimani, P., Koti, M.S.: Medical diagnosis using ensemble classifiers-a novel machine-learning approach. J. Adv. Comput. 1, 9–27 (2013)
Wild, S., et al.: Global prevalence of diabetes estimates for the year 2000 and projections for 2030. Diabetes Care 27(5), 1047–1053 (2004)
Kumari, V.A., Chitra, R.: Classification of diabetes disease using support vector machine. Int. J. Eng. Res. Appl. 3(2), 1797–1801 (2013)
Deekshatulu, B., Chandra, P.: Classification of heart disease using artificial neural network and feature subset selection. Global J. Comput. Sci. Technol. 13(3) (2013)
Zainuddin, Z., Pauline, O., Ardil, C.: A neural network approach in predicting the blood glucose level for diabetic patients. Int. J. Comput. Intell. 5(1), 72–79 (2009)
Adeyemo, A.B., Akinwonmi, A.E.: On the diagnosis of diabetes mellitus using artificial neural network models artificial neural network models. Afr. J. Comput. ICT Ref. Format 4(1), 1–8 (2011)
Federation, I.D.: IDF. International Diabetes Federation. IDF Diabetes Atlas. Accessed 29 November 2014
Association, A.D.: Diagnosis and classification of diabetes mellitus. Diabetes Care 37(Supplement 1), S81–S90 (2014)
Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Pearson education, Boston (2005)
Amato, F., et al.: Artificial neural networks in medical diagnosis. J. Appl. Biomed. 11(2), 47–58 (2013)
Lee, S.-C.L., Embrechts, M.: I-N, Data mining techniques applied to medical information. Inform. Health Soc. Care 25(2), 81–102 (2000)
Karegowda, A.G., et al.: Rule based classification for diabetic patients using cascaded k-means and decision tree C4. 5. Int. J. Comput. Appl. vol. 45 (2012)
Quinlan, J.R.: C4. 5: Programs for Machine Learning, vol. 1. Morgan kaufmann, San Mateo (1993)
Liberti, L., et al.: Euclidean distance geometry and applications. SIAM Rev. 56(1), 3–69 (2014)
Dey, R., et al.: Application of artificial neural network (ANN) technique for diagnosing diabetes mellitus. In: IEEE Region 10 and the Third international Conference on Industrial and Information Systems, ICIIS 2008. IEEE, Kharagpur (2008)
Deng, D., Kasabov, N.: On-line Pattern Analysis by Evolving Self-Organizing Maps. Neurocomputing. 51(1), 87–103. Elsevier, April 2003
Baldwin, J.F., Xie, D.W.: Simple fuzzy logic rules based on fuzzy decision tree for classification and prediction problem. In: Intelligent Information Processing II, pp. 175–184. Springer (2005)
Yegnanarayana, B., Artificial Neural Networks. PHI Learning Pvt. Ltd, New Delhi (2009)
Caballero-Ruiz, E., et al.: Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs. neural networks. In: XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013. Springer (2014)
Yegnanarayana1, B.: Artificial Neural Networks for Pattern Recognition Sadhana, 19(2), 189–238 (1994)
Feizollah, A., et al.: A review on feature selection in mobile malware detection. Digital Investigation 13, 22–37 (2015)
Berglund, E., Sitte, J.: The parameterless self-organizing map algorithm. IEEE Trans. Neural Netw. 17(2), 305–316 (2006)
Lu, J.: A novel feature selection method based on data normalization. In: International Conference on Computer Application and System Modeling (ICCASM). IEEE (2010)
Balsamo, S., et al.: Model-based performance prediction in software development: a survey. IEEE Trans. Softw. Eng. 30(5), 295–310 (2004)
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The authors would like to thank Salahaddin University-Erbil.
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Rashid, T.A., Abdullah, S.M., Abdullah, R.M. (2016). An Intelligent Approach for Diabetes Classification, Prediction and Description. In: Snášel, V., Abraham, A., Krömer, P., Pant, M., Muda, A. (eds) Innovations in Bio-Inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-28031-8_28
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DOI: https://doi.org/10.1007/978-3-319-28031-8_28
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