Diagnose of Chronic Kidney Diseases by Using Naive Bayes Algorithm
DOI:
https://doi.org/10.29304/jqcm.2021.13.2.819Keywords:
Chronic Kidney disease, Naïve Bayes AlgorithmAbstract
Chronic kidney disease (CKD) develops gradually, usually after months or years when the kidneys lose function. In general, it may not be detected before it loses 25% of its functionality. Patients may begin to not recognize kidney failure because kidney failure may not give any symptoms at first. Treatment for kidney failure aims to control the causes and slow the progression of kidney failure. If the treatments are insufficient, the patient is in the end stage of kidney failure and the last treatment is dialysis or a kidney transplant. at this time. Therefore, it is necessary to make an early diagnosis to avoid reaching the stage of kidney failure. We conclude in this paper that the Naive Bayes algorithm is one of the best algorithms for diagnosing diseases with high accuracy of 99.24% and time of 0.003 seconds approximately because it is suitable for this kind of dataset.
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References
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[16] El-Husseini, Z. alabdin, & Flaih, A. (2019). Estimating Mediation and direct effects of the multiple model with application Kidney. Journal of Al-Qadisiyah for Computer Science and Mathematics, 11(4), Stat Page 1-20. https://doi.org/10.29304/jqcm.2019.11.4.617.
[17] Neamah Hussein, K. (2018). Video Frames Edge Detection of Red Blood Cells: A Performance Evaluation. Journal of Al-Qadisiyah for Computer Science and Mathematics, 10(1), Comp Page 16 - 27. https://doi.org/10.29304/jqcm.2018.10.1.347.
[18] A. Mohammed, H. (2018). Design and Implementation of Smart Dust Sensing System for Baghdad City. Journal of Al-Qadisiyah for Computer Science and Mathematics, 10(1), Math Page 80 - 87. https://doi.org/10.29304/jqcm.2018.10.1.353 .
[19] https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease
[2] Singh, V. P., Srivastava, S., & Srivastava, R. (2017). Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests. Technology and Health Care, 25(4), 709-727.
[3] Polat, H., Mehr, H. D., & Cetin, A. (2017). Diagnosis of chronic kidney disease based on support vector machine by feature selection methods. Journal of medical systems, 41(4), 55.
[4] Alassaf, R. A., Alsulaim, K. A., Alroomi, N. Y., Alsharif, N. S., Aljubeir, M. F., Olatunji, S. O., ... & Alturayeif, N. S. (2018, November). Preemptive diagnosis of chronic kidney disease using machine learning techniques. In 2018 international conference on innovations in information technology (IIT) (pp. 99-104). IEEE.
[5] Padmanaban, K. A., & Parthiban, G. (2016). Applying machine learning techniques for predicting the risk of chronic kidney disease. Indian Journal of Science and Technology, 9(29), 1-6.
[6] Devika, R., Avilala, S. V., & Subramaniyaswamy, V. (2019, March). Comparative study of classifier for chronic kidney disease prediction using naive Bayes, KNN, and random forest. In 2019 3rd International conference on computing methodologies and communication (ICCMC) (pp. 679-684). IEEE.
[7] Deepika, B., Rao, V. K. R., Rampure, D. N., Prajwal, P., & Gowda, D. G. (2020). Early prediction of chronic kidney disease by using machine learning techniques. Amer. J. Comput. Sci. Eng. The survey, 8(2), 7.
[8] Qin, J., Chen, L., Liu, Y., Liu, C., Feng, C., & Chen, B. (2019). A Machine Learning Methodology for Diagnosing Chronic Kidney Disease. IEEE Access, 8, 20991-21002.
[9] Tazin, N., Sabab, S. A., & Chowdhury, M. T. (2016, December). Diagnosis of Chronic Kidney Disease using effective classification and feature selection technique. In 2016 International Conference on Medical Engineering, Health Informatics and Technology (Meditec) (pp. 1-6). IEEE.
[10] Kaviani, P., & Dhotre, S. (2017). Short survey on naive Bayes algorithm. International Journal of Advanced Engineering and Research Development, 4(11), 607-611.
[11] Sankara Subbu, R. (2017). Brief Study of Classification Algorithms in Machine Learning.
[12] Balakrishnan, S. (2020). Feature Selection Using Improved Teaching Learning Based Algorithm on Chronic Kidney Disease Dataset. Procedia Computer Science, 171, 1660-1669.
[13] Matter Nassir, L. (2017). A comparison among methods for estimation of the parameter of the Maxwell- Boltzmann distribution using simulation. Journal of Al-Qadisiyah for Computer Science and Mathematics, 6(2), 186-200. Retrieved from https://qu.edu.iq/journalcm/index.php/journalcm/article/view/118.
[14] Abduljabbar Saad, I. (2018). An Efficient Classification Algorithms for Image Retrieval Based Color and Texture Features. Journal of Al-Qadisiyah for Computer Science and Mathematics, 10(1), Comp Page 42 - 53. https://doi.org/10.29304/jqcm.2018.10.1.350.
[15] Hssein, A. (2021). Estimate Reliability Function of Inverse Lindley for Strength – Stress Models. Journal of Al-Qadisiyah for Computer Science and Mathematics, 12(4), Math Page 61-. https://doi.org/10.29304/jqcm.2020.12.4.725.
[16] El-Husseini, Z. alabdin, & Flaih, A. (2019). Estimating Mediation and direct effects of the multiple model with application Kidney. Journal of Al-Qadisiyah for Computer Science and Mathematics, 11(4), Stat Page 1-20. https://doi.org/10.29304/jqcm.2019.11.4.617.
[17] Neamah Hussein, K. (2018). Video Frames Edge Detection of Red Blood Cells: A Performance Evaluation. Journal of Al-Qadisiyah for Computer Science and Mathematics, 10(1), Comp Page 16 - 27. https://doi.org/10.29304/jqcm.2018.10.1.347.
[18] A. Mohammed, H. (2018). Design and Implementation of Smart Dust Sensing System for Baghdad City. Journal of Al-Qadisiyah for Computer Science and Mathematics, 10(1), Math Page 80 - 87. https://doi.org/10.29304/jqcm.2018.10.1.353 .
[19] https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease
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Published
2021-07-09
How to Cite
Abd, N. S., & Abdullah, D. A. (2021). Diagnose of Chronic Kidney Diseases by Using Naive Bayes Algorithm. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(2), Comp Page 46 – 55. https://doi.org/10.29304/jqcm.2021.13.2.819
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Computer Articles
