AReviewonHeartDiseasePredictionusingMachineLearningandDataAnalyticsApproach.pdf

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See discussions, stats, and author profiles for this publication at: A Review on Heart Disease Prediction using Machine Learning and Data Analytics Approach Article in International Journal of Computer Applications · September 2018 DOI: 10.5120/ijca2018917863 CITATION 1 READS 6,322 5 authors , including: Some of the authors of this publication are also working on these related projects: Biometrics View project Internet of Things View project Marimuthu Muthuvel Coimbatore Institute of Technology 18 PUBLICATIONS 20 CITATIONS SEE PROFILE All content following this page was uploaded by Marimuthu Muthuvel on 18 September 2018. The user has requested enhancement of the downloaded file.
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International Journal of Computer Applications (0975 8887) Volume 181 No. 18, September 2018 20 A Review on Heart Disease Prediction using Machine Learning and Data Analytics Approach M. Marimuthu Assistant Professor Coimbatore Institute of Technology Coimbatore M. Abinaya UG Scholar Coimbatore Institute of Technology Coimbatore K. S. Hariesh UG Scholar Coimbatore Institute of Technology Coimbatore K. Madhankumar UG Scholar Coimbatore Institute of Technology Coimbatore V. Pavithra UG Scholar Coimbatore Institute of Technology Coimbatore ABSTRACT Heart is the next major organ comparing to brain which has more priority in Human body. It pumps the blood and supplies to all organs of the whole body. Prediction of occurrences of heart diseases in medical field is significant work. Data analytics is useful for prediction from more information and it helps medical centre to predict of various disease. Huge amount of patient related data is maintained on monthly basis. The stored data can be useful for source of predicting the occurrence of future disease. Some of the data mining and machine learning techniques are used to predict the heart disease, such as Artificial Neural Network (ANN), Decision tree, Fuzzy Logic, K-Nearest Neighbour(KNN), Naïve Bayes and Support Vector Machine (SVM). This paper provides an insight of the existing algorithm and it gives an overall summary of the existing work. Keywords Data mining, Heart disease, Machine learning, Medical centre. 1. INTRODUCTION Heart disease is one of the prevalent disease that can lead to reduce the lifespan of human beings nowadays. Each year 17.5 million people are dying due to heart disease [1]. Life is dependent on component functioning of heart, because heart is necessary part of our body. Heart disease is a disease that affects on the function of heart [2]. An estimate of a person’s risk for coronary heart disease is important for many aspects of health promotion and clinical medicine. A risk prediction model may be obtained through multivariate regression analysis of a longitudinal study [3]. Due to digital technologies are rapidly growing, healthcare centres store huge amount of data in their database that is very complex and challenging to analysis. Data mining techniques and machine learning algorithms play vital roles in analysis of different data in medical centres. The techniques and
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