Optimization The Naive Bayes Classifier Method to diagnose diabetes Mellitus

Lihat/Buka File Repository

Lihat/Buka File Peer Review




World Health Organization (WHO) states that Diabetes Mellitus is the world's top deadly disease. several studies in the health sector including diabetes mellitus have been carried out to detect diseases early. In this study optimization of naive bayes classifier using particle swarm optimization was applied to the data of patients with 2 classes namely positive diabetes mellitus and negative diabetes mellitus and data on patients with 3 classes, those who tested positive for diabetes mellitus type 1, diabetes mellitus type 2 and negative diabetes mellitus.                            

After testing, the algorithm of Naive Bayes Classifier and Naive Bayes Classifier based on Particle Swarm Optimization, the results obtained are the Naive Bayes Classifier method for 2 classes and 3 classes each producing an accuracy value of 78.88% and 68.50%. but after adding Particle Swarm Optimization the value of accuracy increased respectively to 82.58% and 71, 29%. The classification results for 2 classes have an accuracy value higher than 3 classes with a difference of 11.29% 

Kata Kunci: Diabetes Mellitus, naive bayes classifier, Particle Swarm Optimization


Bidang ilmu
Data Mining


[1] WHO. (2016). WHO​. Retrieved from diabetes programme. http://www.who.int/diabete/en/

[2] Vijayan, V. V., & Anjali, C. (2015, December). Prediction and diagnosis of diabete mellitus—A machine learning approach. In 2015 IEEE Recent Advances in Intellige      Computational Systems (RAICS)​ (pp. 122-127). IEEE. 

[3] Zaccardi, F., Webb, D. R., Yates, T., & Davies, M. J. (2016). Pathophysiology of type 1 and type 2 diabetes mellitus: a 90-year perspective. Postgraduate medical journal​, 92 (1084), 63-69.

[4] Gao, C. Z., Cheng, Q., He, P., Susilo, W., & Li, J. (2018). Privacy-preserving Naive Bayes classifiers secure against the substitution-then-comparison attack. Information Sciences​, 444​, 72-88. 

[5] Vijayarani, S., & Dhayanand, S. (2015). Liver disease prediction using SVM and Naïve Bayes algorithms. International Journal of Science, Engineering and Technology  Research (IJSETR)​, 4​(4), 816-820.

[6] Brannen, J. (2017). Mixing Methods.  Qualitative and Quantitative Research. 

[7] Vembandasamy, K., Sasipriya, R., & Deepa, E. (2015). Heart diseases detection using Naive Bayes algorithm. International Journal of Innovative Science, Engineering & Technology​, 2​(9), 441-444.

[8] Rani, S., & Kautish, S. (2018, June). Association Clustering and Time Series Based  Data Mining in Continuous Data for Diabetes Prediction. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS)​ (pp. 1209-1214). IEEE.

[9] Perveen, S., Shahbaz, M., Guergachi, A., & Keshavjee, K. (2016). Performance analysis of data mining classification techniques to predict diabetes. Procedia Computer Science​, 82, 115-121.