Data Smoothing Numerical Methods and Their Applications in Unsupervised Learning for Prediction of Diabetes in Patients
Satish Kumar Soni1, R S Thakur2, A K Gupta3
1Satish Kumar Soni, PhD (pursuing) in Computer Sciences, Barkatullah University, Bhopal. MCA from RGPV University, Bhopal. (M.P) India.
2Dr. Ramjeevan Singh Thakur, Associate Professor(MCA), MANIT, Bhopal. (M.P) India.
3Dr. Anil Kumar Gupta, Dept., of Computer Science & Applications Barkatullah University, Bhopal. (M.P) India.
Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1695-1699 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8196078919/19©BEIESP | DOI: 10.35940/ijitee.I8196.078919
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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: Machine Learning in its fullest can provide much more accurate and enhanced analysis for medical diagnosis. In this paper we are trying to portray how the data related to diabetes can be used to predict if a person has diabetes or not. In more specific way this paper will explore the utilization of numerical methods smoothing with unsupervised learning to predict the early signs of disease like diabetes and rest.
keyword: Unsupervised Machine Learning, Numerical Methods, Functional Smoothing, Diabetes Prediction, Clustering.
Scope of the Article: Standards for IoT Applications