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Research Article
A Machine Learning Methodology for Diagnosing Chronic Kidney Disease
A.Saimanthra1
N.Seneca2
R.Soniya3
M.Subalakshmi4
M.Ramesh5
1234Student, Department of computer science and engineering, Vivekananda College ofEngineering for women, Tiruchengode , Namakkal District, Tamil Nadu, 637205, India. 5Assistant professor, Department of computer science and engineering, Vivekananda College ofEngineering for women, Tiruchengode, Namakkal District, Tamil Nadu,637205,India.
Published Online: May-June 2022
Pages: 308-313
Cite this article
No DOIReferences
1. M. M. Hossain et al., "Mechanicalanisotropy evaluation in kidney cortex utilizing ARFI
2. top relocation: Preclinical approval and pilot in vivo clinical outcomes in kidney allografts," IEEE Trans. Ultrason. Ferr., vol. 66, no.
3, pp. 551-562, Mar.2019.
3. E. Hodneland et al., "In vivo identification of persistent kidney illness utilizing tissue distortion fields from dynamic MR imaging," IEEE
Trans. BioMed. Eng., vol. 66, no. 6, pp. 1779-1790, Jun. 2019.
4. G. R. Vasquez-Morales et al., "Logical forecast of ongoing renal illness in the colombianpopulace utilizing neural organizations and
case- based thinking," IEEE Access, vol. 7, pp. 152900- 152910, Oct. 2019.
5. N. Almansour et al., "Neural organizationand backing vector machine for the forecast of constant kidney illness: A relative report,"
Comput. Biol. Drug., vol. 109, pp. 101-111, Jun. 2019
6. M. Alloghani et al., "Utilizations of AI strategies for programming learning and early expectation of understudies' exhibition," in Proc.
Int. Conf. Delicate Computing in Data Science, Dec. 2018, pp. 246-258.
7. L. Du et al., "An AI based way to deal with distinguish safeguarded wellbeing data in Chinese clinical text," Int. J. Drug. Illuminate.,
vol. 116, pp. 24-32, Aug. 2018
8. R. Abbas et al., "Characterization of fetaltrouble and hypoxia utilizing AI draws near," in Proc.Int. Conf. Insightful Computing, Jul.
2018, pp. 767- 776
9. M. Mahyoub, M. Randles, T. Bread cookand P. Yang, "Examination investigation of AI calculations to rank alzheimer's infection
hazard factors by significance," in Proc. eleventh Int. Conf. Improvements in eSystems Engineering, Sep. 2018.
10. Q. Zou et al., "Foreseeing diabetes mellitus with AI strategies," Front. Genet., vol. 9, Nov. 2018
11. Z. Gao et al., "Determination of diabetic retinopathy utilizing profound neural organizations," IEEE Access, vol. 7, pp. 3360-3370, Dec.2018.
2. top relocation: Preclinical approval and pilot in vivo clinical outcomes in kidney allografts," IEEE Trans. Ultrason. Ferr., vol. 66, no.
3, pp. 551-562, Mar.2019.
3. E. Hodneland et al., "In vivo identification of persistent kidney illness utilizing tissue distortion fields from dynamic MR imaging," IEEE
Trans. BioMed. Eng., vol. 66, no. 6, pp. 1779-1790, Jun. 2019.
4. G. R. Vasquez-Morales et al., "Logical forecast of ongoing renal illness in the colombianpopulace utilizing neural organizations and
case- based thinking," IEEE Access, vol. 7, pp. 152900- 152910, Oct. 2019.
5. N. Almansour et al., "Neural organizationand backing vector machine for the forecast of constant kidney illness: A relative report,"
Comput. Biol. Drug., vol. 109, pp. 101-111, Jun. 2019
6. M. Alloghani et al., "Utilizations of AI strategies for programming learning and early expectation of understudies' exhibition," in Proc.
Int. Conf. Delicate Computing in Data Science, Dec. 2018, pp. 246-258.
7. L. Du et al., "An AI based way to deal with distinguish safeguarded wellbeing data in Chinese clinical text," Int. J. Drug. Illuminate.,
vol. 116, pp. 24-32, Aug. 2018
8. R. Abbas et al., "Characterization of fetaltrouble and hypoxia utilizing AI draws near," in Proc.Int. Conf. Insightful Computing, Jul.
2018, pp. 767- 776
9. M. Mahyoub, M. Randles, T. Bread cookand P. Yang, "Examination investigation of AI calculations to rank alzheimer's infection
hazard factors by significance," in Proc. eleventh Int. Conf. Improvements in eSystems Engineering, Sep. 2018.
10. Q. Zou et al., "Foreseeing diabetes mellitus with AI strategies," Front. Genet., vol. 9, Nov. 2018
11. Z. Gao et al., "Determination of diabetic retinopathy utilizing profound neural organizations," IEEE Access, vol. 7, pp. 3360-3370, Dec.2018.
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