ISSN 2394-5125
 

Research Article 


EFFECTIVE PREDICTION OF CARDIOVASCULAR DISEASE USING CLUSTER OF MACHINE LEARNING ALGORITHMS

G. Jignesh Chowdary, Suganya. G, Premalatha. M.

Abstract
Cardiovascular diseases are one of the diseases that account for the loss of millions of lives each year. Lack of early prediction is the primary reason for the loss of lives, and this encourages researchers to develop intelligent systems for better prediction. In this paper, a novel ensemble methodology is introduced which uses the voting of Logistic Regression(LR), Random Forest(RF), Artificial Neural Network activated with ReLU function(NNR), K-Nearest Neighbors (KNN) and Gaussian Naive Bayes(GNB) to predict the possibility of heart disease. The model is developed using Python-based Jupyter Notebook and Flask and is trained using the standard dataset from Kaggle. The model is tested and evaluated based on accuracy, precision, specificity, sensitivity, error. Testing witnessed an accuracy of 89% and a precision of 91.6%, along with a sensitivity of 86% and specificity of 91%. The results upon comparison with the individual models witness the better accuracy of using ensemble modeling and hence a better prediction leading to life-saving.

Key words: Cardiovascular disease, Machine Learning, Feature Scaling, cardiac disease prediction, Attribute selection.


 
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How to Cite this Article
Pubmed Style

G. Jignesh Chowdary, Suganya. G, Premalatha. M. EFFECTIVE PREDICTION OF CARDIOVASCULAR DISEASE USING CLUSTER OF MACHINE LEARNING ALGORITHMS. JCR. 2020; 7(19): 4142-4150. doi:10.31838/jcr.07.19.485


Web Style

G. Jignesh Chowdary, Suganya. G, Premalatha. M. EFFECTIVE PREDICTION OF CARDIOVASCULAR DISEASE USING CLUSTER OF MACHINE LEARNING ALGORITHMS. http://www.jcreview.com/?mno=127585 [Access: September 15, 2020]. doi:10.31838/jcr.07.19.485


AMA (American Medical Association) Style

G. Jignesh Chowdary, Suganya. G, Premalatha. M. EFFECTIVE PREDICTION OF CARDIOVASCULAR DISEASE USING CLUSTER OF MACHINE LEARNING ALGORITHMS. JCR. 2020; 7(19): 4142-4150. doi:10.31838/jcr.07.19.485



Vancouver/ICMJE Style

G. Jignesh Chowdary, Suganya. G, Premalatha. M. EFFECTIVE PREDICTION OF CARDIOVASCULAR DISEASE USING CLUSTER OF MACHINE LEARNING ALGORITHMS. JCR. (2020), [cited September 15, 2020]; 7(19): 4142-4150. doi:10.31838/jcr.07.19.485



Harvard Style

G. Jignesh Chowdary, Suganya. G, Premalatha. M (2020) EFFECTIVE PREDICTION OF CARDIOVASCULAR DISEASE USING CLUSTER OF MACHINE LEARNING ALGORITHMS. JCR, 7 (19), 4142-4150. doi:10.31838/jcr.07.19.485



Turabian Style

G. Jignesh Chowdary, Suganya. G, Premalatha. M. 2020. EFFECTIVE PREDICTION OF CARDIOVASCULAR DISEASE USING CLUSTER OF MACHINE LEARNING ALGORITHMS. Journal of Critical Reviews, 7 (19), 4142-4150. doi:10.31838/jcr.07.19.485



Chicago Style

G. Jignesh Chowdary, Suganya. G, Premalatha. M. "EFFECTIVE PREDICTION OF CARDIOVASCULAR DISEASE USING CLUSTER OF MACHINE LEARNING ALGORITHMS." Journal of Critical Reviews 7 (2020), 4142-4150. doi:10.31838/jcr.07.19.485



MLA (The Modern Language Association) Style

G. Jignesh Chowdary, Suganya. G, Premalatha. M. "EFFECTIVE PREDICTION OF CARDIOVASCULAR DISEASE USING CLUSTER OF MACHINE LEARNING ALGORITHMS." Journal of Critical Reviews 7.19 (2020), 4142-4150. Print. doi:10.31838/jcr.07.19.485



APA (American Psychological Association) Style

G. Jignesh Chowdary, Suganya. G, Premalatha. M (2020) EFFECTIVE PREDICTION OF CARDIOVASCULAR DISEASE USING CLUSTER OF MACHINE LEARNING ALGORITHMS. Journal of Critical Reviews, 7 (19), 4142-4150. doi:10.31838/jcr.07.19.485