Classification of Liver Disorders Diagnosis using Naïve Bayes Method

Abstract

Liver diseases pose a significant health challenge, necessitating robust predictive tools for early diagnosis. This study aims to determine the predictive performance of Naive Bayes classifier, one of the data mining algorithms, in the classification of liver diseases. The study applied 5, 10 and 20-fold cross-validation method. Trying to determine the effect of the cross-validation (CV) method used on the classification performance, this study used the "BUPA" dataset in the UCI Machine Learning Repository database for this purpose. The dataset consists of 6 variables and 345 examples. Orange program was used for data analysis. The study showed that the accuracy of the Naive bayes method were 64.6%, 66.7% and 64.3%, respectively. Accordingly, it can be said that the 10-fold CV method performs better. Compared to similar studies, it can be claimed that the analysis results obtained with the Orange program are better.

Keywords

• Data mining
• machine learning
• Naive Bayes
• liver diseases

References

1. [1] M.Kayri, İ.Kayri and M.T. Gencoglu, “The performance comparison of Multiple Linear Regression, Random Forest and Artificial Neural Network by using photovoltaic and atmospheric data”, IEEE 14th International Conference on Engineering of Modern Electric Systems (EMES), pp.1-4, June 2017.
2. [2] H. C. Koh and G. Tan, “Data mining applications in healthcare”, Journal of Healthcare Information Management, vol.19, no.2, pp.65-72, 2011.
3. [3] A. Peña-Ayala, “Educational data mining: A survey and a data mining-based analysis of recent works”, Expert systems with applications, vol.41, no.4, pp.1432-1462, 2014.
4. [4] M., Kayri and, İ. Kayri, “The comparison of Gini and Twoing algorithms in terms of predictive ability and misclassification cost in data mining: an empirical study”, International Journal of Computer Trends and Technology (IJCTT), vol. 27, no. 1, pp.21-30, 2015.
5. [5] Ö. B. Güre, M. Kayri and F.Erdoğan, “Analysis of Factors Effecting PISA 2015 Mathematics Literacy via Educational Data Mining”, Education & Science/Egitim ve Bilim, vol.45, no.202, pp.393-415, 2020.
6. [6] M. Sharma, “Data mining: A literature survey”, International Journal of Emerging Research in Management & Technology, vol.3, no.2, pp.1-4, 2014.
7. [7] R. H. Khokhar, R. Chen, B.C. Fung and S.M. Lui, “Quantifying the costs and benefits of privacy-preserving health data publishing”, Journal of biomedical informatics, vol.50, pp.107-121, 2014.
8. [8] S. Bahramirad, A. Mustapha and M. Eshraghi,” Classification of liver disease diagnosis: A comparative study”, IEEE 2013 Second International Conference on Informatics & Applications (ICIA), pp.42-46, September 2013.

9. [9] P. Kumar and R.S. Thakur, “Liver disorder detection using variable-neighbor weighted fuzzy K nearest neighbor approach”, Multimedia Tools and Applications, vol.80, pp.16515-16535, 2021.
10. [10] T. R. Baitharu and S.K. Pani, “Analysis of data mining techniques for healthcare decision support system using liver disorder dataset”, Procedia Computer Science, vol.85, pp.862-870, 2016.
11. [11] P. Kuppan and N. Manoharan, “A Tentative analysis of Liver Disorder using Data Mining Algorithms J48, Decision Table and Naive Bayes”, International Journal of Computing Algorithm, vol.6, no.1, pp.2278-239, 2017.
12. [12] B. V. Ramana, M. S. P. Babu and N.B. Venkateswarlu, “A critical study of selected classification algorithms for liver disease diagnosis”, International Journal of Database Management Systems, vol.3, no.2, pp.101-114, 2011.
13. [13] R. Kalaviselvi and G. Santhoshni, “A Comparative Study on Predicting the Probability of Liver Disease”, International Journal of Engineering Research & Technology (IJERT), vol.8, no. 10, pp.560-564, 2019.
14. [14] R. H. Lin, “An intelligent model for liver disease diagnosis”, Artificial Intelligence in Medicine, vol.47 no.1, pp.53-62, 2009.
15. [15] S. N. N. Alfisahrin and T. Mantoro, “Data mining techniques for optimization of liver disease classification” IEEE 2013 International Conference on Advanced Computer Science Applications and Technologies, pp.379-384, December 2013.
16. [16] M. Abdar, “A survey and compare the performance of IBM SPSS modeler and rapid miner software for predicting liver disease by using various data mining algorithms”, Cumhuriyet University Faculty of Science Science Journal (CSJ), vol.36, no.3, pp.3230-3241, 2015.
17. [17] H. Sug, “Improving the prediction accuracy of liver disorder disease with oversampling”, Proc. of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics, Wisconsin United States, January, 25-27, 2012.
18. [18] H. Subhani and S. Badugu, “A study of liver disease classification using data mining and machine learning algorithms”, in Learning and Analytics in Intelligent Systems : Proc. of the the Advances in Decision Sciences, Image Processing, Security and Computer Vision: International Conference on Emerging Trends in Engineering (ICETE), Hyderabad, India, March 22–23, 2019, George A. Tsihrintzis, Maria Virvou, Lakhmi C. Jain, Eds. Berlin: Springer,2019. Vol. 2, pp. 630-640.
19. [19] M. K. Ram, C. Sujana, R. Srinivas and G. S. N. Murthy, “A fact-based liver disease prediction by enforcing machine learning algorithms”. in Advances in Intelligent Systems and Computing Proc. Of the Computational Vision and Bio-Inspired Computing: ICCVBIC, Coimbatore, India, November 19-20, 2020. Janusz Kacprzyk Eds. Berlin: Springer, 2020. pp.567-586
20. [20] S.Wang, J. Ren and R. Bai, “A semi-supervised adaptive discriminative discretization method improving discrimination power of regularized Naive Bayes”, Expert Systems with Applications, vol. 225, no. 120094, pp. 1-7, 2023.
21. [21] S. Vijayarani and S. Dhayanand, “Liver disease prediction using SVM and Naïve Bayes algorithms”, International Journal of Science, Engineering and Technology Research (IJSETR), vol.4, no.4, pp.816-820, 2012.
22. [22] T. M., Kamruzzaman, M. S., Mahbub and M. A. Hakim, “A Structured Method For Predicting Liver Disease Using Machine Learning Techniques & Improvements In Correctness”, IEEE 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp.01-07, July 2021.
23. [23] N., Nahar and F. Ara, “Liver disease prediction by using different decision tree techniques”, International Journal of Data Mining & Knowledge Management Process, vol.8 no.2, pp.01-09, 2018 .
24. [24] K. Al-Aidaroos, A. A., Bakar and Z. Othman, “Medical data classification with Naive Bayes approach”, Information Technology Journal, vol.11, no.9, pp.1166-1174, 2012.
25. [25] R. Bhardwaj, R. Mehta and P. Ramani, “A comparative study of classification algorithms for predicting liver disorders”, Intelligent Computing Techniques for Smart Energy Systems. Lecture Notes in Electrical Engineering, vol 607. Springer, Singapore.
26. [26] UCI Machine Learning Repository: BUPA data Set. Available: https://archive.ics.uci.edu/ml/datasets/Higher+Education+Students+Performance+Evaluation+Dataset#
27. [27] J. McDermott and R.S. Forsyth, “Diagnosing a disorder in a classification benchmark”, Pattern Recognition Letters, vol.73, pp. 41-43, 2016.
28. [28] Orange programming. Available: https://orangedatamining.com/
29. [29] N. Friedman, D. Geiger and M. Goldszmidt, M.,”Bayesian network classifiers”, Machine learning, vol.29, pp.131-163, 1997.
30. [30] I. Wickramasinghe and H. Kalutarage, “Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation”, Soft Computing, vol.25, no.3, pp.2277-2293, 2021.
31. [31] X. Wu and V. Kumar, The top ten algorithms in data mining, CRC press, 2009.
32. [32] A. Choi, N. Tavabi, and A. Darwiche, “Structured features in Naive Bayes classification” in the AAAI Conference on Artificial Intelligence, vol.3 no.1, pp.3233-3240, February, 2016.
33. [33] Z. Muda, W. Yassin, M.N. Sulaiman and N.I. Udzir, “A K-Means and Naive Bayes learning approach for better intrusion detection”, Information technology journal, vol.10 no.3, pp.648-655, 2011.
34. [34] M. M. Saritas and A. Yasar, “Performance analysis of ANN and Naive Bayes classification algorithm for data classification”, International journal of intelligent systems and applications in engineering, vol.7 no.2, pp.88-91, 2019.
35. [35] S. S. Nikam, “A comparative study of classification techniques in data mining algorithms”, Oriental Journal of Computer Science and Technology, vol.8 no.1, pp.13-19, 2015.
36. [36] R. Blanquero, E. Carrizosa, E., P.Ramírez-Cobo and M.R. Sillero-Denamiel, “Variable selection for Naïve Bayes classification”, Computers & Operations Research, vol.135, no.105456, pp.1-11, 2021.
37. [37] J. Han and M. Kamber, M. Data mining: concepts and techniques, Second Edi. TM KSIDMA Systems, ed., Morgan Kaufmann Publisher, 2006
38. [38] S. Mukherjee and N. Sharma, “Intrusion detection using naive Bayes classifier with feature reduction”, Procedia Technology, vol.4, pp.119-128, 2012.
39. [39] H. Chen, S. Hu, R. Hua and X. Zhao, “Improved naive Bayes classification algorithm for traffic risk management”, EURASIP Journal on Advances in Signal Processing, vol. 2021 no.1, pp.1-12, 2021.
40. [40] S. K. Depren, Ö. E. Aşkın and E. Öz, “Identifying the classification performances of educational data mining methods: A case study for TIMSS”, Educational Sciences: Theory & Practice, vol.17, no.5, pp.1605-1623, 2017.
41. [41] G. Kaur and E.N. Oberai, “A review article on Naive Bayes classifier with various smoothing techniques”, International Journal of Computer Science and Mobile Computing, vol.3, no.10, pp.864-868, 2014.
42. [42] S. Xu, “Bayesian Naïve Bayes classifiers to text classification”, Journal of Information Science, vol.44, no.1, pp.48-59, 2018.
43. [43] D. Berrar, Cross-validation, Encyclopedia of Bioinformatics and Computational Biology, Vol. 1, Elsevier, pp. 542–545, 2018.
44. [44] H. Şevgin and E. Önen, “Comparison of Classification Performances of MARS and BRT Data Mining Methods: ABİDE- 2016 Case”, Education & Science/Egitim ve Bilim, vol.47, no.211, pp.195-222, 2022. [45] G. Akgül, A.A. Çelik, Z.E. Aydın and Z.K. Öztürk, “Hipotiroidi Hastalığı Teşhisinde Sınıflandırma Algoritmalarının Kullanımı”, Bilişim Teknolojileri Dergisi, vol.13, no.3, pp.255-268, 2020.
45. [46] T. S. Sujana, N. M. S. Rao and R. S. Reddy, “An efficient feature selection using parallel cuckoo search and naïve Bayes classifier”, IEEE 2017 International Conference on Networks & Advances in Computational Technologies (NetACT), pp.167-172, July 2017.
46. [47] C. Ruengdetkhachorn and D. Lohpetch, “Feature Selection using Parallel Cuckoo Algorithm with Naïve Bayes Classifier based on Two Different Strategies”, IEEE 22nd International Computer Science and Engineering Conference (ICSEC), pp.1-4, November 2018,
47. [48] D. Pradhan, B.B. Misra, B. Sahoo and D.K. Jena, “Evolutionary Teaching-Learning Based Modified Polynomial Classifier”, IEEE 19th OITS International Conference on Information Technology (OCIT), pp.313-318, December 2021.
48. [49] M. Z. Alam, M. S. Rahman and M. S. Rahman, “A Random Forest based predictor for medical data classification using feature ranking”, Informatics in Medicine Unlocked, vol.15, no.100180, pp.1-11, 2019.