Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease


Dwi Yuni Utami(1*), Elah Nurlelah(2), Noer Hikmah(3),


(1) 
(2) 
(3) 
(*) Corresponding Author

Abstract


Liver disease is an inflammatory disease of the liver and can cause the liver to be unable to function as usual and even cause death. According to WHO (World Health Organization) data, almost 1.2 million people per year, especially in Southeast Asia and Africa, have died from liver disease. The problem that usually occurs is the difficulty of recognizing liver disease early on, even when the disease has spread. This study aims to compare and evaluate Naive Bayes algorithm as a selected algorithm and Naive Bayes algorithm based on Genetic Algorithm (GA) and Bagging to find out which algorithm has a higher accuracy in predicting liver disease by processing a dataset taken from the UCI Machine Learning Repository database (GA). University of California Invene). From the results of testing by evaluating both the confusion matrix and the ROC curve, it was proven that the testing carried out by the Naive Bayes Optimization algorithm using Algortima Genetics and Bagging has a higher accuracy value than only using the Naive Bayes algorithm. The accuracy value for the Naive Bayes algorithm model is 66.66% and the accuracy value for the Naive Bayes model with attribute selection using Genetic Algorithms and Bagging is 72.02%. Based on this value, the difference in accuracy is 5.36%.
Keywords: Liver Disease, Naïve Bayes, Genetic Agorithms, Bagging.


Full Text:

PDF

References


Abrianto, P. M. C. (2018). PENERAPAN METODE K-MEANS CLUSTERING UNTUK PENGELOMPOKKAN PASIEN PENYAKIT LIVER. JATI (Jurnal Mahasiswa Teknik Informatika), 2(2), 247–255.

Ayudhitama, A. P., & Pujianto, U. (2020). ANALISA 4 ALGORITMA DALAM KLASIFIKASI PENYAKIT LIVER MENGGUNAKAN RAPIDMINER. JIP (Jurnal Informatika Polinema), 6(2), 1–9.

Dhamodharan, S. (2014). Liver Disease Prediction Using Bayesian Classification. 4th National Conference on Advanced Computing, Applications & Technologies, May, 1–3. https://ijact.in/index.php/ijact/article/view/443/378

Ghosh, S. R., & Waheed, S. (2017). Analysis of classification algorithms for liver disease diagnosis. Journal of Science Technology and Environment Informatics, 5(1), 360–370. https://doi.org/10.18801/jstei.050117.38

Krisnandi, K., & Agung, H. (2017). Implementasi Algoritma Genetika Untuk Memprediksi Waktu Dan Biaya Pengerjaan Proyek Konstruksi. Jurnal Ilmiah FIFO, 9(2), 90. https://doi.org/10.22441/fifo.2017.v9i2.001

Nahar, N., & Ara, F. (2018). LIVER DISEASE PREDICTION BY USING DIFFERENT DECISION TREE TECHNIQUES. International Journal of Data Mining & Knowledge Management Process, 8(2), 01–09. https://doi.org/10.5121/ijdkp.2018.8201

Noviandi. (2018). Implementasi Algoritma Decision Tree C4.5 Untuk Prediksi Penyakit Diabetes. Inohim, 6(1), 1–5.

Pakhale, H., & Xaxa, D. K. (2016). A Survey on Diagnosis of Liver Disease Classification. International Journal of Engineering and Techniques, 2(3), 132–138. http://www.ijetjournal.org

Priya, M. B., Juliet, P. L., & Tamilselvi, P. R. (2018). Performance Analysis of Liver Disease Prediction Using Machine Learning Algorithms. International Research Journal of Engineering and Technology(IRJET), 5(1), 206–211. www.irjet.net

Pusporani, E., Qomariyah, S., & Irhamah. (2019). Klasifikasi Pasien Penderita Penyakit Liver dengan Pendekatan Machine Learning. Inferensi, 2(March), 25–32.

Rafsanjani, R. G., Hidayat, N., & Dewi, R. K. (2018). Diagnosis Penyakit Hati Menggunakan Metode Naive Bayes Dan Certainty Factor. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK), 2(11), 4478–4482.

Riyanto, U. (2018). ANALISIS PERBANDINGAN ALGORITMA NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM MENGKLASIFIKASIKAN JUMLAH PEMBACA ARTIKEL ONLINE. Jurnal Teknik Informatika (JIKA) Universitas Muhammadiyah Tangerang, 62–72.

Saleh, A. (2015). Implementasi Metode Klasifikasi Naïve Bayes Dalam Memprediksi Besarnya Penggunaan Listrik Rumah Tangga. Creative Information Technology Journal, 2(3), 207–217.

Zulfikar, W. B., & Lukman, N. (2016). Perbandingan Naive Bayes Classifier Dengan Nearest Neighbor Untuk Identifikasi Penyakit Mata. Jurnal Online Informatika, 1(2), 82–86. https://doi.org/10.15575/join.v1i2.33




DOI: https://doi.org/10.31289/jite.v4i1.3793

Article Metrics

Abstract view : 0 times
PDF - 0 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.