Data Mining Algorithms on Prediction of Cardiovascular Diseases
Archana Singh1, Seema Mahajan2
1Archana Singh, Research Scholar, Indus University, Ahmedabad, Gujarat, India.
2Seema Mahajan, Indus University, Ahmedabad, Gujarat, India.
Manuscript received on 12 August 2019. | Revised Manuscript received on 19 August 2019. | Manuscript published on 30 September 2019. | PP: 4846-4853 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6887098319/2019©BEIESP | DOI: 10.35940/ijrte.C6887.098319
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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: In the age of data generation known as Big Data, where data is produced in enormous amount, managing it has become a big challenge and along with this drawing information from the gathered data is equally important and challenging. Inferring relationships and predicting patterns from theses structured and unstructured data is now an area of research for researchers. And the data mining techniques have evolved as a tool for generating results and deducing conclusions. These mining algorithms find their applicability in almost every domain likewise understanding market segment, fraud detection, trend analysis, healthcare sector, education sector and many more. Looking at the wide range of applicability, in this paper, a brief overview of data mining algorithms is discussed. This discussion comprises of different data mining algorithms, their mathematical modelling, their evaluation methods, and their limitations. To support the fact a case study is conducted on a cardiovascular disease dataset and the measures of these mining techniques are compared.
Index terms: SVM, Naïve Bayes, Random Forest, ROC Analysis, Confusion Matrix, Visual Matrices, Performance Matrix.
Scope of the Article: Data Mining