Improved Prediction of Diabetes based on Glucose Levels in blood using Data Science Algorithms
Sowjanya V1, Divyambica CH2, Gopinath P3, Vamsidhar M4, B.Vijaya Babu5, Professor6
1Sowjanya. V, Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Amaravathi (A.P), India.
2Divyambica. Ch, Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Guntur (A.P), India.
3Gopinath. P, Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Guntur (A.P), India.
4Vamsidhar. M, Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada (A.P), India.
5Dr. Vijaya Babu. B, Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Vijayawada (A.P), India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 877-881 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6193048419/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: In this paper, diabetes disease is predicted more accurately based on Glucose Levels in the blood of the person using Data Science Algorithms Logistic Regression, Decision Tree, Naive Bayes and K-Means Data Science Algorithms. The analysis is done based on 09 attributes and 399 observations. Finally, a comparative analysis is done for the results of above stated data science algorithms and the results with Logistic Regression Algorithm proved to be better with 80% accuracy.
Keywords: Diabetes Disease, Data Science Algorithms, Logistic Regression, Decision Tree, Naive Bayes, K-Means Algorithms.
Scope of the Article: Data Analytics Modelling and Algorithms