An Optimize Weights Naïve Bayes Model for Early Detection of Diabetes

Oman Somantri, Ratih Hafsarah Maharrani, Linda Perdana Wanti

Abstract


This research proposes a method to optimize the accuracy of the Naïve Bayes (NB) model by optimizing weight using a genetic algorithm (GA). The process of giving optimal weight is carried out when the data will be input into the analysis process using NB. The research stages were conducted by preprocessing the data, searching for the classic naïve Bayes model, optimizing the weight, applying the hybrid model, and as the final stage, evaluating the model. The results showed an increase in the accuracy of the proposed model, where the naïve Bayes classical model produced accuracy rate of 87.69% and increased to 88.65% after optimization using GA. The results of the study conclude that the proposed optimization model can increase the accuracy of the classification of early detection of diabetes.

Keywords


optimize weights; naïve bayes; diabetes; genetic algorithm

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References


Anwar, F., Qurat-Ul-Ain, Ejaz, M. Y., & Mosavi, A. (2020). A comparative analysis on diagnosis of diabetes mellitus using different approaches – A survey. Informatics in Medicine Unlocked, 21, 100482. doi: https://doi.org/10.1016/j.imu.2020.100482

Candra Permana, B. A., & Dewi Patwari, I. K. (2021). Komparasi Metode Klasifikasi Data Mining Decision Tree dan Naïve Bayes Untuk Prediksi Penyakit Diabetes. Infotek : Jurnal Informatika Dan Teknologi, 4(1), 63–69. doi: https://doi.org/10.29408/jit.v4i1.2994

Friedman, N., Geiger, D., Goldszmidt, M., Provan, G., Langley, P., & Smyth, P. (1997). Bayesian Network Classifiers *. Machine Learning, 29, 131–163. https://doi.org/10.1023/A:1007465528199

Islam, M. M. F., Ferdousi, R., Rahman, S., & Bushra, H. Y. (2020). Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques. Advances in Intelligent Systems and Computing, 992, 113–125. doi: https://doi.org/10.1007/978-981-13-8798-2_12

Kotu, V., & Deshpande, B. (2019). Model Evaluation. In Data Science (pp. 263–279). Elsevier. doi: https://doi.org/10.1016/b978-0-12-814761-0.00008-3

M M Faniqul Islam; Rahatara Ferdousi. (2020). UCI Machine Learning Repository: Early stage diabetes risk prediction dataset. Data Set. https://archive.ics.uci.edu/ml/datasets/Early+stage+diabetes+risk+prediction+dataset.

Melanie, M. (1996). An introduction to genetic algorithms. Cambridge, Massachusetts London, England. doi: https://doi.org/10.1016/S0898-1221

Mok, C. H., Kwok, H. H. Y., Ng, C. S., Leung, G. M., & Quan, J. (2021).

Health State Utility Values for Type 2 Diabetes and Related Complications in East and Southeast Asia: A Systematic Review and Meta-Analysis. Value in Health. doi: https://doi.org/10.1016/j.jval.2020.12.019

Mujumdar, A., & Vaidehi, V. (2019). Diabetes Prediction using Machine Learning Algorithms. Procedia Computer Science, 165, 292–299. doi: https://doi.org/10.1016/j.procs.2020.01.047

Nurdiana, N., & Algifari, A. (2020). Studi Komparasi Algoritma Id3 Dan Algoritma Naive Bayes Untuk Klasifikasi Penyakit Diabetes Mellitus. INFOTECH Journal, 6(2), 18–23. https://ejournal.unma.ac.id/index.php/infotech/article/view/816

Ridwan, A. (2020). Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Penyakit Diabetes Mellitus. Jurnal SISKOM-KB (Sistem Komputer Dan Kecerdasan Buatan), 4(1), 15–21. doi: https://doi.org/10.47970/siskom-kb.v4i1.169

Shivakumar, B. L., & Alby, S. (2014). A survey on data-mining technologies for prediction and diagnosis of diabetes. Proceedings - 2014 International Conference on Intelligent Computing Applications, ICICA 2014, 167–173. doi: https://doi.org/10.1109/ICICA.2014.44

Shrivastava, R. K., Ramakrishna, S., & Hota, C. (2019). Game theory based modified naïve-bayes algorithm to detect DoS attacks using Honeypot. 2019 IEEE 16th India Council International Conference, INDICON 2019 - Symposium Proceedings, 1–4. doi: https://doi.org/10.1109/INDICON47234.2019.9030355

Somantri, O., & Apriliani, D. (2019). Opinion mining on culinary food customer satisfaction using naïve bayes based-on hybrid feature selection. Indonesian Journal of Electrical Engineering and Computer Science, 15(1), 468–475. doi: https://doi.org/10.11591/ijeecs.v15.i1

Tripathi, A., Yadav, S., & Rajan, R. (2019). Naive Bayes Classification Model for the Student Performance Prediction. 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2019, 1548–1553. doi: https://doi.org/10.1109/ICICICT46008.2019.8993237

Tripathi, G., & Kumar, R. (2020). Early Prediction of Diabetes Mellitus Using Machine Learning. ICRITO 2020 - IEEE 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), 1009–1014. doi: https://doi.org/10.1109/ICRITO48877.2020.9197832

Vigneswari, D., Kumar, N. K., Ganesh Raj, V., Gugan, A., & Vikash, S. R. (2019). Machine Learning Tree Classifiers in Predicting Diabetes Mellitus. 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, 84–87. doi: https://doi.org/10.1109/ICACCS.2019.8728388

Wang, Z. Z., & Sobey, A. (2020). A comparative review between Genetic Algorithm use in composite optimisation and the state-of-the-art in evolutionary computation. Composite Structures, 233, 111739. doi: https://doi.org/10.1016/j.compstruct.2019.111739




DOI: http://dx.doi.org/10.35671/telematika.v15i1.1307

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Telematika
ISSN: 2442-4528 (online) | ISSN: 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto, Indonesia


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