Predicting Heart Disease Using Machine Learning Tools and Techniques
C Ramya1, Manoj Kumar D S2
1C Ramya, UG Scholar, Department of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai (Tamil Nadu), India.
2Manoj Kumar D S, Assistant Professor, Department of CSE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai (Tamil Nadu), India.
Manuscript received on 25 April 2019 | Revised Manuscript received on 07 May 2019 | Manuscript Published on 17 May 2019 | PP: 387-390 | Volume-7 Issue-6S4 April 2019 | Retrieval Number: F10760476S419/2019©BEIESP
Open Access | Editorial and Publishing 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: Machine learning is closely associated with process statistics, that focuses on creating predictions by utilizing computers. The study of mathematical improvement delivers strategies, theory and application domains to the sector of machine learning. Data processing could be a field of study inside machine learning, and focuses on explorative information analysis through unsupervised learning. Demand for machine is growing tremendously to the explosion in data volume. IT industry is continuously inching forward to fulfil the demand by developing multiple tools and incorporating various techniques for machine learning process. This project deals with exploring and investigating different tools available in the IT market. Also, it deals with applying linear regression algorithm on heart disease dataset using various tools and techniques. As a result, we will be able to discuss the advantages and disadvantages of each practical tool that is being used in this project.
Keywords: Machine Learning, Supervised Learning, Logistic Regression Algorithm.
Scope of the Article: Machine Learning