IDENTIFICATION OF CHRONIC KIDNEY DISEASE USING NAIVE BAYES, ADABOOST, AND RANDOM FOREST LEARNING METHODS

  • Raras Tyasnurita (1*) Institut Teknologi Sepuluh Nopember
  • Shafira Widya Hapsari (2)

  • (*) Corresponding Author
Keywords: Chronic Kidney Disease, Machine Learning, Classification, Naive Bayes, AdaBoost, Random Forest

Abstract

Chronic kidney disease is a decrease in function in the kidneys where the condition leads to kidney damage. This disease causes damage to the body's immunity, because the body fails to maintain fluid balance. Therefore, it becomes a critical need to identify whether a patient is a sufferer of chronic kidney disease or not. The classification methods used in this study are Naive Bayes, AdaBoost, and Random Forest. Recently, proper early recognition is needed to detect chronic kidney disease to prevent delays in its treatment. Given the large number of chronic kidney disease cases that occur, this study is expected to be an effort to control the increase in sufferers. The results showed that the Naive Bayes approach achieved 95.4% accuracy, which increased to 98.6% after AdaBoost was implemented, and Random Forest led at 99.3%.

Author Biography

Raras Tyasnurita, Institut Teknologi Sepuluh Nopember

Information Systems Study Program Lecturer

References

K. Daenen, A. Andries, D. Mekahli, A. Van Schepdael, F. Jouret, and B. Bammens, “Oxidative stress in chronic kidney disease,” Pediatr. Nephrol., vol. 34, no. 6, pp. 975–991, 2019.

H. Widiani, “Penyakit ginjal kronik stadium V akibat nefrolitiasis,” Intisari Sains Medis, vol. 11, no. 1, pp. 160–164, 2020.

I. Fadilla, P. P. Adikara, and R. S. Perdana, “Klasifikasi Penyakit Chronic Kidney Disease (CKD) Dengan Menggunakan Metode Extreme Learning Machine (ELM),” J. Pengemb. Teknol. Inf. dan Ilmu Komput. e-ISSN, vol. 2548, p. 964X, 2018.

A. W. Kusumo, Perbedaan Penyebab Gagal Ginjal Antara Usia Tua Dan Muda Pada Penderita Penyakit Ginjal Kronik Stadium V Yang Menjalani Hemodialisis Di Rsud Dr. Moewardi. Surakarta: Universitas Muhammadiyah Surakarta, 2010.

R. C. Deo, “Machine learning in medicine,” Circulation, vol. 132, no. 20, pp. 1920–1930, 2015,.

H. Kriplani, B. Patel, and S. Roy, “Prediction of chronic kidney diseases using deep artificial neural network technique,” in Computer Aided Intervention and Diagnostics in Clinical and Medical Images, Springer, 2019, pp. 179–187.

A. Saha, A. Saha, and T. Mittra, “Performance measurements of machine learning approaches for prediction and diagnosis of chronic kidney disease (CKD),” ACM Int. Conf. Proceeding Ser., pp. 200–204, 2019.

W. Yunus, “Algoritma K-Nearest Neighbor Berbasis Particle Swarm Optimization Untuk Prediksi Penyakit Ginjal Kronik,” J. Cosphi, vol. 2, no. 2, 2018.

T. Arifin and D. Ariesta, “Prediksi Penyakit Ginjal Kronis Menggunakan Algoritma Naive Bayes Classifier Berbasis Particle Swarm Optimization,” J. Tekno Insentif, vol. 13, no. 1, pp. 26–30, 2019.

A. Ilham, “Hybrid Metode Boostrap Dan Teknik Imputasi Pada Metode C4-5 Untuk Prediksi Penyakit Ginjal Kronis,” J. Stat. Univ. Muhammadiyah Semarang, vol. 8, no. 1, 2020.

K. Dharmarajan, “Prediction of Chronic Kidney Disease using Classification techniques," Parishodh Journal, Page No : 1420,” no. March, 2020.

D. Dua and C. Graff, “UCI machine learning repository, 2017,” URL http//archive. ics. uci. edu/ml, vol. 37, 2019.

A. Fatima, N. Nazir, and M. G. Khan, “Data Cleaning In Data Warehouse: A Survey of Data Pre-processing Techniques and Tools,” Int. J. Inf. Technol. Comput. Sci., vol. 9, no. 3, pp. 50–61, 2017.

B. K. Khotimah, M. Miswanto, and H. Suprajitno, “Optimization of feature selection using genetic algorithm in naïve Bayes classification for incomplete data,” Int. J. Intell. Eng. Syst., vol. 13, no. 1, pp. 334–343, 2020.

S. Zeynu and S. Patil, “Prediction of Chronic Kidney Disease Using Data Mining Feature Selection and Ensemble Method,” Journal of Data Mining in Genomics, vol. 9, no. 1, pp. 1–9, 2018.

B. Tiemens, R. Wagenvoorde, and C. Witteman, “Why every clinician should know Bayes’ rule,” Heal. Prof. Educ., no. xxxx, 2020.

Arif-Ul-Islam and S. H. Ripon, “Rule Induction and Prediction of Chronic Kidney Disease Using Boosting Classifiers, Ant-Miner and J48 Decision Tree,” 2nd Int. Conf. Electr. Comput. Commun. Eng. ECCE 2019, no. February, 2019.

D. S. Sisodia and A. Verma, “Prediction performance of individual and ensemble learners for chronic kidney disease,” Proc. Int. Conf. Inven. Comput. Informatics, ICICI 2017, no. Icici, pp. 1027–1031, 2018.

J. Ali, R. Khan, N. Ahmad, and I. Maqsood, “Random Forests and Decision Trees,” Int. J. Comput. Sci. Issues, vol. 9, no. 5, pp. 272–278, 2012.

J. Huang and C. X. Ling, “Using AUC and accuracy in evaluating learning algorithms,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 3, pp. 299–310, 2005.

Published
2020-08-01
How to Cite
Tyasnurita, R., & Hapsari, S. W. (2020). IDENTIFICATION OF CHRONIC KIDNEY DISEASE USING NAIVE BAYES, ADABOOST, AND RANDOM FOREST LEARNING METHODS. JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), 6(1), 115-120. https://doi.org/10.33480/jitk.v6i1.1403
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