Diagnosis of Chronic Kidney Disease using Naïve Bayes algorithm Supported by Stage Prediction using eGFR
A Victor Ikechukwu, K Nivedha, N M Prakruthi, Farheen Fathima, R Harini, L Shamitha
Affiliations Department of CSE, Maharaja Institute of Technology Mysore, Visvesvaraya Technological University, Belagavi,
The proliferation of data and availability of open source tools has simplified the diagnosis of diseases such as CKD (Chronic Kidney Disease). As one of the types of kidney disease which results in malfunctioning of kidney, it is paramount to effectively diagnose such diseases to prevent degeneration of vital organs in the body. Despite the advancements in the field of medical imaging, there exists no permanent cure for CKD, but the risk can be mitigated to a larger extent if detected at the early stage. This paper proposes a hybrid approach to early detection of chronic kidney disease by using Naïve Bayes classifier and eGFR (estimated Glomerular Filtration Rate). Naïve Bayes which works on the principle of conditional probability was used to predict whether a patient has CKD or not based on clinical symptoms, and the stage was determined using the eGFR formula. Results were promising as the model was able to predict the prevalence of CKD as well as the stage in which the patient was in. Although we were able to develop a web-based application using machine learning algorithms to aid in the diagnosis of CKD by serving as a “self-diagnostic” tool for medical practitioners, improvements could be made to ensure that the model works according to established ground truth by nephrologists.
A Victor Ikechukwu, K Nivedha, N M Prakruthi, Farheen Fathima, R Harini, L Shamitha." Diagnosis of Chronic Kidney Disease using Naïve Bayes algorithm Supported by Stage Prediction using eGFR". International Journal of Computer Engineering In Research Trends (IJCERT), ISSN:2349-7084, Vol. 7, Issue 10, pp.6-12, October - 2020, URL:https://ijcert.org/ems/ijcert_papers/V7I1002.pdf,
Keywords : Naïve Bayes, Random Forest, eGFR, CKD, Medical Diagnosis
 H. Zhang, C. L. Hung, W. C. C. Chu, P. F. Chiu, and C. Y. Tang, “Chronic Kidney Disease Survival Prediction with Artificial Neural Networks,” Proc. - 2018 IEEE Int. Conf. Bioinforma. Biomed. BIBM 2018, pp. 1351–1356, 2019.
 R. A. Alassaf et al., “Preemptive Diagnosis of Chronic Kidney Disease Using Machine Learning Techniques,” Proc. 2018 13th Int. Conf. Innov. Inf. Technol. IIT 2018, pp. 99–104, 2019.
 T. Mahboob, A. Ijaz, A. Shahzad, and M. Kalsoom, “Handling Missing Values in Chronic Kidney Disease Datasets Using KNN, K-Means and K-Medoids Algorithms,” ICOSST 2018 - 2018 Int. Conf. Open Source Syst. Technol. Proc., pp. 76–81, 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.
 V. Kunwar, K. Chandel, A. S. Sabitha, and A. Bansal, “Chronic Kidney Disease analysis using data mining classification techniques,” Proc. 2016 6th Int. Conf. - Cloud Syst. Big Data Eng. Conflu. 2016, pp. 300–305, 2016.
 H. A. Wibawa, I. Malik, and N. Bahtiar, “Evaluation of Kernel-Based Extreme Learning Machine Performance for Prediction of Chronic Kidney Disease,” 2018 2nd Int. Conf. Informatics Comput. Sci. ICICoS 2018, no. x, pp. 33–36, 2019.
 G. Kaur and A. Sharma, “Mining Algorithms In Hadoop,” no. Icici, 2017.
 G. W.H.S.D, “Performance Evaluation on Machine Learning Classification Techniques for Disease (CKD),” Ieee, pp. 291–296, 2017.
 M. P. N. M. Wickramasinghe, D. M. Perera, and K. A. D. C. P. Kahandawaarachchi, “Dietary prediction for patients with Chronic Kidney Disease (CKD) by considering blood potassium level using machine learning algorithms,” 2017 IEEE Life Sci. Conf. LSC 2017, vol. 2018-Janua, pp. 300–303, 2018.
 R. Devika, S. V. Avilala, and V. Subramaniyaswamy, “Comparative study of classifier for chronic kidney disease prediction using naive bayes, KNN and random forest,” Proc. 3rd Int. Conf. Comput. Methodol. Commun. ICCMC 2019, no. Iccmc, pp. 679–684, 2019.
 A. Charleonnan, T. Fufaung, T. Niyomwong, W. Chokchueypattanakit, S. Suwannawach, and N. Ninchawee, “Predictive analytics for chronic kidney disease using machine learning techniques,” 2016 Manag. Innov. Technol. Int. Conf. MITiCON 2016, pp. MIT80–MIT83, 2017.
 L. Jena and R. Swain, “Work-in-Progress: Chronic Disease Risk Prediction Using Distributed Machine Learning Classifiers,” Proc. - 2017 Int. Conf. Inf. Technol. ICIT 2017, pp. 170–173, 2018.
 E. Avci, S. Karakus, O. Ozmen, and D. Avci, “Performance comparison of some classifiers on Chronic Kidney Disease data,” 6th Int. Symp. Digit. Forensic Secur. ISDFS 2018 - Proceeding, vol. 2018-Janua, pp. 1–4, 2018.
 A. Banerjee, A. Noor, N. Siddiqua, and M. N. Uddin, “Food Recommendation using Machine Learning for Chronic Kidney Disease Patients,” 2019 Int. Conf. Comput. Commun. Informatics, ICCCI 2019, pp. 1–5, 2019.
 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, pp. 7–9, 2019.
 B. Khan, R. Naseem, F. Muhammad, G. Abbas, and S. Kim, “An empirical evaluation of machine learning techniques for chronic kidney disease prophecy,” IEEE Access, vol. 8, pp. 55012–55022, 2020.
 V. I. Agughasi, Y. Dk, and S. Das M, “Early Prognosis of Heart Failure from Clinical Symptoms using K-Means and Naïve Bayes Algorithms,” vol. 9, no. 7, pp. 55–61, 2020.
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