Diabetes Classification Using ID3 and Naïve Bayes Algorithms

Jassim, Khalid; Saleh, Hadeel M.

doi:10.37652/juaps.2022.171841

Document Type : Research Paper

Authors

College of computer Science and IT, University of Anbar, Ramadi, Anbar, Iraq

https://doi.org/10.37652/juaps.2022.171841

Abstract

Diabetes can be defined as a chronic disease identified by high levels of blood glucose that result from issues in the way insulin is generated, the way insulin works, or both those reasons. The aim of this research is to propose a technique using the Decision Tree (ID3) and Naive Bayes to categorize diabetes and reduce classification errors by increasing the accuracy of the classification. The results of the proposed method were evaluated by comparing them with other results through the application of the proposed system to Pima India Diabetes data set, obtained from the UCI database site. The experimental results show that the ID3 recorded a precision ratio of 91% and the naive class corrected it to 94% for the same number of the test group.

Keywords

Main Subjects

Computer

References

[1] Smitha, P., Shaji, L., & Mini, M. G. (2011). A review of medical image classification techniques. In International conference on VLSI, Communication & Intrumrnataiom (pp. 34-38).

[2] Canlas, R. D. (2009). Data mining in healthcare: Current applications and issues. School of Information Systems & Management, Carnegie Mellon University, Australia.

[3] Porter, T., & Green, B. (2009). Identifying diabetic patients: a data mining approach. AMCIS 2009 Proceedings, 500.

[4]Naik, J., & Patel, S. (2014). Tumor detection and classification using decision tree in brain MRI. International Journal of Computer Science and Network Security (IJCSNS), 14(6), 87.

[5] Mao, Y., Chen, Y., Hackmann, G., Chen, M., Lu, C., Kollef, M., & Bailey, T. C. (2011, December). Medical data mining for early deterioration warning in general hospital wards. In Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on (pp. 1042-1049). IEEE.

[6] Esmaeli, M. (2011). A Scalable Parallel Algorithm for Decision Support from Multidimensional Sequence Data.

[7]Selvakuberan, K., Kayathiri, D., Harini, B., & Devi, M. I. (2011, April). An efficient feature selection method for classification in health care systems using machine learning techniques. In Electronics Computer Technology (ICECT), 2011 3rd International Conference on (Vol. 4, pp. 223-226). IEEE.

[8]Velu, C. M., & Kashwan, K. R. (2013, February). Visual data mining techniques for classification of diabetic patients. In Advance Computing Conference (IACC), 2013 IEEE 3rd International (pp. 1070-1075). IEEE.

[9]Bashir, S., Qamar, U., Khan, F. H., & Javed, M. Y. (2014, December). An Efficient Rule-Based Classification of Diabetes Using ID3, C4. 5, & CART Ensembles. In Frontiers of Information Technology (FIT), 2014 12th International Conference on (pp. 226-231). IEEE.

[10] Aishwarya, S., & Anto, S. (2014). A Medical Expert System based on Genetic Algorithm and Extreme Learning Machine for Diabetes Disease Diagnosis. International Journal of Science, Engineering and Technology Research (IJSETR), 3(5).

[11]Sanakal, R., & Jayakumari, T. (2014). Prognosis of diabetes using data mining approach-fuzzy C means clustering and support vector machine. International Journal of Computer Trends and Technology, 11(2), 94-8.

[12] Durairaj, M., & Kalaiselvi, G. (2015). Prediction of diabetes using soft computing techniques-A survey. International journal of scientific and technology research, 4(3), 190-192.

Journal of University of Anbar for Pure Science

Diabetes Classification Using ID3 and Naïve Bayes Algorithms

References

References

Volume 12, Issue 3
December 2018
Page 38-46

Diabetes Classification Using ID3 and Naïve Bayes Algorithms

References

References

Volume 12, Issue 3December 2018Page 38-46

Volume 12, Issue 3
December 2018
Page 38-46