Diabetes Impacted Cardiovascular Disease Prediction using Machine Learning
C.Akash Mahadevan1, S. Kanishka2, Saisurya. S3, V. Arun4
1C.Akash Mahadevan, B. Tech CSE, SRM Institute of Science and Technology, Chennai, India.
2S.Kanishka, B. Tech CSE, SRM Institute of Science and Technology, Chennai, India.
3Saisurya.S, B. Tech CSE, SRM Institute of Science and Technology, Chennai, India.
4V.Arun, CSE, SRM Institute of Science and Technology, Chennai, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 4376-4378 | Volume-9 Issue-2, December, 2019. | Retrieval Number: A1681109119/2019©BEIESP | DOI: 10.35940/ijeat.A1681.129219
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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: Utilizing big data growth in biological and health communities, an accurate analogy of medical data can benefit the detection of diabetes impacting cardiovascular diseases. Using k-Means clustering (kMC) algorithm for structured data of heart disease patients, we narrow down to cardiovascular diseases impacted by diabetes. To our knowledge, none of the previous work focused on predicting heart diseases specifically for diabetes patients. Contrasted to multiple other prediction algorithms, the accuracy of predicting in our proposed algorithm is faster than that of other prediction systems for cardiovascular diseases.
Keywords: Cardiovascular diseases, Diabetes, Prediction.