Main Article Content
Among the world population, the Disease of Heart is one of the biggest mortality and morbidity causes. This disease's precise prediction and early detection might decline rate of mortality rate certainly. Learning machines are utilized to consider several problems in the science of information. In Fortune, one of efficient methods for classification is naïve bayes (NB) is because of the ability of it for learning inherent features of data. Although, generally such method groups data with just one single that makes this less efficient relatively in several classes for big classification issue. In the article, we present the tree structural naïve bayes (Tree-NB) that classifies big classification in small classifications with utilizing structure of tree. The particular classifier is adjusted after division for every small classification. By several classifiers that are employed, Tree-NB is able to complement each other in performance of classification as well as one classifier issue is solved. As all several classifiers are end-to-end frameworks, automatically Tree-NB is able to learn nonlinear relationship among output and input data with no extraction of feature. For verifying our model validity, we compare modern methods with Tree-NB by utilizing dataset of UCI. Experimental results illustrate that Tree- NB is able to obtain the higher performance in less time of training. Average Tree- NB accuracy is 1.19 % higher than the other modern methods also it possesses higher average recall and precision.
- 1. Katarya, R., & Meena, S. K. (2021). Machine learning techniques for heart disease prediction: a comparative study and analysis. Health and Technology, 11(1), 87-97.
- 2. Mary, M. M. A., & Beena, T. L. A. (2020). Heart disease prediction using machine learning techniques: A survey. Int. J. Res. Appl. Sci. Eng. Technol., 8(10), pp. 441-447.
- 3. Repaka, A. N., Ravikanti, S. D., & Franklin, R. G. (2019). Design and implementing heart disease prediction using naives Bayesian. In 2019 3rd International conference on trends in electronics and informatics (ICOEI), pp. 292-297.
- 4. Bajaj, P., & Gupta, P. (2014). Review on heart disease diagnosis based on data mining techniques. International Journal of Science and Research (IJSR), 3(5).
- 5. Alsheref, F. K., & Gomaa, W. H. (2019). Blood diseases detection using classical machine learning algorithms. International Journal of Advanced Computer Science and Applications (IJACSA). Blood, 10(7).
- 6. Muibideen, M., & Prasad, R. (2020). A Fast Algorithm for Heart Disease Prediction using Bayesian Network Model. arXiv preprint arXiv:2012.09429.
- 7. Porkodi, K., Aishwarya,, A., Divya, R., Indhuja, K., & Manasa, S. (2020). Efficent classification of heart disease using machine learning algorithm. Journal of Xi’an Shiyou University, Natural Science Edition, 17(7), pp. 59-66.
- 8. Vanitha Guda, S. K., & Shivani, C. (2020). Heart Disease Prediction Using Hybrid Technique. Journal of Interdisciplinary Cycle Research. 6(5)920-927.
- 9. Kavitha, M., Gnaneswar, G., Dinesh, R., Sai, Y. R., & Suraj, R. S. (2021). Heart Disease Prediction using Hybrid machine Learning Model. In 2021 6th International Conference on Inventive Computation Technologies (ICICT), pp. 1329-1333.
- 10. Mohan, S., Thirumalai, C., & Srivastava, G. (2019). Effective heart disease prediction using hybrid machine learning techniques. IEEE access, 7, pp. 81542-81554.
- 11. Khourdifi, Y., & Bahaj, M. (2019). Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization. International Journal of Intelligent Engineering and Systems, 12(1), pp. 242-252.
- 12. Singh, A., & Kumar, R. (2020). Heart disease prediction using machine learning algorithms. In 2020 international conference on electrical and electronics engineering (ICE3), pp. 452-457.
- 13. Sajjadnia, Zeinab, Raof Khayami, and Mohammad Reza Moosavi. "Preprocessing Breast Cancer Data to Improve the Data Quality, Diagnosis Procedure, and Medical Care Services." Cancer Informatics 19 (2020): 1176935120917955.
- 14. Ren, X., Gu, H., & Wei, W. (2021). Tree-RNN: Tree structural recurrent neural network for network traffic classification. Expert Systems with Applications, 167, 114363.
- 15. Durairaj, M., & Sivagowry, S. (2014). A pragmatic approach of preprocessing the data set for heart disease prediction. international journal of Innovative Research in computer and communication Engineering, 2(11), 6457-6465.
- 16. Ali Hasan Ali 2023. Smart Fire System using IOT. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES. 4, 3 (Apr. 2023), 88-113.
- 17. Hasan Ali , A., M Jebur, H., & Alzamili, Z. marid J. (2023). DESIGN OF A VIRTUAL REALITY SIMULATOR OF A DORMITORY BY USING EXCEL VBA. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 4(5), 99-119. https://doi.org/10.17605/OSF.IO/CMY8X.