Rahma Fitria, Desvina Yulisda, Mutammimul Ula


Data mining explores a huge amount of data to extract the information to be meaningful. In the field of public health, data mining hold a crucial contribution in predicting disease in early stage. In order to detect diseases, the patients need to conduct various tests. In the context of disease predicion, Data mining techniques aims to reduce the test that patients need to accomplish. Also the techniques is used to increase the accuracy rate of detection. Nowadays, diabetes attacks many adults in the world. Moreover, in order to reduce the number of adult having diabetes, an effective and efficient diabetes detection mechanism should be found. This report will apply some data mining techniques on diabetes dataset that has been downloaded at UCI Machine Learning Repository.Three kind of classification algorithm such as Naïve Bayes Classifier, Multilayer Perceptrons (MLP’s) and Desicion Tree (J.48) have been performed on this dataset. Obtained outcomes indicated that Naïve Bayes Classifier achieved the highest accuracy with 76,30%. As the result, this algorithm is a good method to classify and diagnose diabetes diseases on studying dataset.

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Alpan, K., & Ilgi, G. S. (2020). Classification of Diabetes Dataset with Data Mining Techniques by Using WEKA Approach. 4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020 - Proceedings.

American Diabetes Association. (2005). Diabetes Mellitus and Other Categories of Description of Diabetes. World Health, 28(Suppl 1), 224102.

Centers for Disease Control and Prevention. (2017). Diabetes and Prediabetes and improve the health of all people with diabetes . Retrieved from

Chaves, L., & Marques, G. (2021). applied sciences Data Mining Techniques for Early Diagnosis of Diabetes. Appl. Sci., 11(2218), 1–12.

Flach, P. A. (2004). Naive Bayesian Classification of Structured Data. Machine Learning, 57(1), 233–269.

Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques (Third Edit). Waltham: Morgan Kaufmann.

Hasdyna, N., & Dinata, R. K. (2020). Analisis Matthew Correlation Coefficient pada K-Nearest Neighbor dalam Klasifikasi Ikan Hias. INFORMAL: Informatics Journal, 5(2), 57-64.

Iyer, A., Jeyalatha, S., & Sumbaly, R. (2015). Diagnosis of Diabetes Using Classification Mining Techniques. International Journal of Data Mining & Knowledge Management Process (IJDKP), 5(1), 1–14.

Kumar, V., Mishra, B. K., Mazzara, M., Thanhx, D. N. H., & Verma, A. (2019). Prediction of malignant & benign breast cancer: A data mining approach in healthcare applications. ArXiv, 1–8.

Rahman, R. M., & Afroz, F. (2013). Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis. Journal of Software Engineering and Applications, 2013(March), 85–97.

Shuja, M., Mittal, S., & Zaman, M. (2020). Effective Prediction of Type II Diabetes Mellitus Using Data Mining Classifiers and SMOTE. In Advances in Computing and Intelligent Systems (pp. 195–211).

Thirumal, P. C., & Nagarajan, N. (2015). Utilization Of Data Mining Techniques For Diagnosis Of Diabetes Mellitus - A Case Study. ARPN Journal of Engineering and Applied Sciences, 10(1), 8–13.

Ula, M., Ulva, A. F., & Mauliza, M. (2021). Implementasi Machine Learning Dengan Model Case Based Reasoning Dalam Mendiagnosa Gizi Buruk Pada Anak”. Jurnal Informatika Kaputama (JIK), 5(2), 333-339.


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