Research Article

DOI :10.26650/JODA.1389471   IUP :10.26650/JODA.1389471    Full Text (PDF)

Multi-Class Classification with the Gaussian Naive Bayes Algorithm

Ayşe Çınar

Classification is a data mining technique involving supervised machine learning and is the process of predicting the class of data or dataset whose class is unknown using existing data with defined class. Supervised learning occurs during this classification process as a result of how this technique parses the data according to predetermined outputs. The Naive Bayes classifier is a type of machine learning algorithm and an approach that adopts Bayes’ theorem by combining theoretically obtained preliminary information with new information. The most obvious advantages of this classifier are its simple algorithm and high accuracy rate. The aim of this study is to create a classification model using the Gaussian Naive Bayes algorithm and to evaluate the obtained prediction results. For this purpose, the study first theoretically considers within its scope the Naive Bayes classifier and then carries out an application on a dataset using the Gaussian Naive Bayes algorithm as one of the types of this classifier in order to create a classification model, which is the subject of the study. Operations were carried out for the classification model using Python, an open-source programming language. The dataset used within the scope of the study was obtained from the University of California Irvine (UCI) Machine Learning Repository website. The purpose for creating the dataset is to determine the different types of Erythemato-squamous disease (ESD). In line with developing technologies, the number of studies demonstrating the ability to make fast and reliable disease prediction using machine learning techniques are increasing daily.

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  • Aggarwal, S., & Kaur, D. (2013). Naive Bayes Classifier with Various Smoothing Techniques for Text Documents.International Journal of Computer Trends and Technology (IJCTT), 4(4), 873-876. Volume4/issue-4/IJCTT-V4I4P187.pdf google scholar
  • Ahmed, M., Kashem, M. A., Rahman, M., & Khatun, S. (2020). Review and Analysis of Risk Factor of Maternal Health in Remote Area Using the Internet of Things (IoT), Part of the Lecture Notes in Electrical Engineering book series (LNEE), volume 632, Springer. google scholar
  • Berrar, D.( January 2018), Bayes’ Theorem and Naive Bayes Classifier, Reference Module in Life Sciences,’_Theorem_and_Naive_Bayes_Classifier#fullTextFileContent google scholar
  • Bilgin, G., & Çifçi A. (2021). Eritematöz Skuamöz Hastalıkların Teşhisinde Makine Öğrenme Algoritmaları Performanslarının Değerlendirilmesi. Zeki Sistemler Teori ve Uygulamaları Dergisi, 4(2), 195-202. google scholar
  • İlter, N., & Güvenir, H. (1997). Dermatology, google scholar
  • İlter, N., Güvenir, H., & Demiroz, G. (1998). Learning Differential Diagnosis of Erythemato-Squamous Diseases Using Voting Feature Intervals. Artificial Intelligence in Medicine, 13(3), 147-165. google scholar
  • Ismail, M., Hassan,N., & Bafjaish,S.S. (2020). Comparative Analysis of Naive Bayesian Techniques in Health-Related for Classification Task. JSCDM-Journal of Soft Computing and Data Mining, 1(2), 1-10. google scholar
  • Khuda, I. E. (2021). Innovative Teaching Pedagogy for Teaching and Learning of Bayes’Theorem. Journal of Science and Engineering, CUSJE, 18(1), 61-71, Çankaya University, 63-71. google scholar
  • Luukka P. (2011). A New Nonlinear Fuzzy Robust PCA Algorithm and Similarity Classifier in Classification of Medical Datasets. International Journal of Fuzzy Systems, 13(3), 153-162. google scholar
  • Meiriza, A., Lestari, E., Putra, P., Monaputri, A. & Lestari, D.A. (2019). Prediction Graduate Student Use Naive Bayes Classifier, Advances in Intelligent Systems Research Series. Volume 172, Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019), Atlantis Press. google scholar
  • Orhan, U & Adem, K. (2012, October 29-December 01). Naive Bayes Yönteminde Olasılık Çarpanlarının Etkileri [Conference presentation]. Elektrik - Elektronik ve Bilgisayar Mühendisliği Sempozyumu, 29 Kasım - 01 Aralık 2012, Bursa. google scholar
  • Putatunda, S. (2020, July 2-4). A Hybrid Deep Learning Approach For Diagnosis Of The Erythemato-Squamous Disease[Conference presentation]. IEEE International Conference On Electronics, Computing And Communication Technologies, Bangalore, India, 1-6. google scholar
  • Rashid, A.N.M.B., Ahmed, M., Sikos, L.F., Haskell-Dowland, P. (2020). A Novel Penalty-Based Wrapper Objective Function for Feature Selection In Big Data Using Cooperative Co-Evolution. IEEE Access, 8, 150113-150129. google scholar
  • Salmi, N. & Rustam, Z. (2019). Naive Bayes Classifier Models for Predicting the Colon Cancer, 9th Annual Basic Science International Conference 2019 (BaSIC 2019), IOP Conf. Series: Materials Science and Engineering 546 (2019) 052068, IOP Publishing. google scholar
  • Sarkar P. (2023). Naive Bayes in Machine Learning [Examples, Models, Types], blog/data-science/naive-bayes-in-machine-learning google scholar
  • Shastri, S., Kour, P., Kumar, S., Singh, K., Mansotra, V. (2021). GBoost: A Novel Grading-AdaBoost Ensemble Approach for Automatic Identification of Erythemato-Squamous Disease. International Journal of Information Technology, 13(3), 959-971. google scholar
  • Vadapalli,P. (2022). Naive Bayes Classifier: Pros & Cons, Applications & Types Explained, https://www.upgrad. com/blog/naive-bayes-classifier/ google scholar
  • Verma, A.K., Pal, S., Kumar, S. (2020). Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method—A Comparative Study. Applied Biochemistry and Biotechnology, 190(2), 341-359. google scholar
  • Wibawa, A.P., Kurniawan, A.C., Murti, D.M.P., Adiperkasa, R.P., Putra, S.M., Kurniawan, S.A. & Nugraha, Y.R. (2019). Naıve Bayes Classifier for Journal Quartile Classification. JES, 7(2), 91-99, ijes.v7i2.10659 google scholar
  • Wu, J., Cai, Z. & Zhu, X. (2013, August 4-9). Self-adaptive probability estimation for Naive Bayes Classification[Conference presentation]. International Joint Conference on Neural Networks (IJCNN), U.S.A. google scholar


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Çınar, A. (2023). Multi-Class Classification with the Gaussian Naive Bayes Algorithm. Journal of Data Applications, 0(2), 1-13.


Çınar A. Multi-Class Classification with the Gaussian Naive Bayes Algorithm. Journal of Data Applications. 2023;0(2):1-13.


Çınar, A. Multi-Class Classification with the Gaussian Naive Bayes Algorithm. Journal of Data Applications, [Publisher Location], v. 0, n. 2, p. 1-13, 2023.

Chicago: Author-Date Style

Çınar, Ayşe,. 2023. “Multi-Class Classification with the Gaussian Naive Bayes Algorithm.” Journal of Data Applications 0, no. 2: 1-13.

Chicago: Humanities Style

Çınar, Ayşe,. Multi-Class Classification with the Gaussian Naive Bayes Algorithm.” Journal of Data Applications 0, no. 2 (Jul. 2024): 1-13.

Harvard: Australian Style

Çınar, A 2023, 'Multi-Class Classification with the Gaussian Naive Bayes Algorithm', Journal of Data Applications, vol. 0, no. 2, pp. 1-13, viewed 25 Jul. 2024,

Harvard: Author-Date Style

Çınar, A. (2023) ‘Multi-Class Classification with the Gaussian Naive Bayes Algorithm’, Journal of Data Applications, 0(2), pp. 1-13. (25 Jul. 2024).


Çınar, Ayşe,. Multi-Class Classification with the Gaussian Naive Bayes Algorithm.” Journal of Data Applications, vol. 0, no. 2, 2023, pp. 1-13. [Database Container],


Çınar A. Multi-Class Classification with the Gaussian Naive Bayes Algorithm. Journal of Data Applications [Internet]. 25 Jul. 2024 [cited 25 Jul. 2024];0(2):1-13. Available from: doi: 10.26650/JODA.1389471


Çınar, Ayşe. Multi-Class Classification with the Gaussian Naive Bayes Algorithm”. Journal of Data Applications 0/2 (Jul. 2024): 1-13.


Published Online07.03.2024


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