Predicting and Staging Chronic Kidney Disease of Diabetes (Type-2) Patient using Machine Learning Algorithms
Setu Basak1, Md. Mahbub Alam2, Aniruddha Rakshit3, Ahmed Al Marouf4, Anup Majumder5
1Setu Basak*, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
2Md. Mahbub Alam, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
3Aniruddha Rakshit, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
4Ahmed Al Marouf, Department of CSE, Daffodil International University, Dhaka, Bangladesh.
5Anup Majumder, Department of CSE, Jahangirnagar University, Dhaka, Bangladesh.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 206-209 | Volume-8 Issue-12, October 2019. | Retrieval Number: L35721081219/2019©BEIESP | DOI: 10.35940/ijitee.L3572.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Mortality because of unending kidney disease increments essentially in recent years. Nowadays, about 422 million patients are suffering from diabetes among them around 30 percent of patients with Type 1 (adolescent beginning) diabetes and around 10 to 40 percent of those with Type 2 (grown-up beginning) diabetes in the end will experience the negative impacts of kidney damage. It is evident, that early detection of Chronic Kidney Disease (CKD) can mitigate the level of damage in the adulthood. In this paper, we have presented a comparative analysis based on the performance of five different algorithms-Naive Bayes (NB), In-stance Based Learning (IBK), Random Forest (RF), Decision Stump (DS) and Decision Tree (J48) for predicting CKD of diabetes patients only by urine test. Among all the algorithms the IBK gives the best result. Our comparison of different algorithms will help people with diabetes to find out if they are having CKD or not.
Keywords: Kidney Disease Staging, Cross-Validation, Morbidity and Mortality, Albuminuria, Proteinuria
Scope of the Article: Machine Learning