Abstract
Cardiovascular disease (CVD) stands as a type of disease that incorporates the heart or the blood vessels. Over the past few decades, it has become the common result of a death in underdeveloped, developing as well as in developed countries. This field is thus, “data rich” but “knowledge poor”. Here, data mining holds great potential and immensely helps the health system to systematically use the data and analytics to recognize the inefficiencies thus, reducing the practice of burdensome tests. Author presents the Heart Disorder Prognosis System for accurate detection of heart disease which has been derived from distinctive analysis among several data mining algorithms. The presence of heart disorder in a sufferer is forecasted by digging out appealing patterns from the datasets. The datasets used for analysis are fetched from the UCI Machine Learning Repository, namely Cleveland Clinical Foundations and the Hungarian Institute of Cardiology. This paper tries to introduce the methodology, implementation and analysis of Decision Tree (ID3), Support Vector Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) algorithm for detection of heart diseases. The conclusion is induced on the basis of accuracy and ROC value. ID3 algorithm gives better performance over other algorithms for both the datasets.
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Acknowledgement
The authors intend to convey gratefulness to Dr. Andras Janosi, M.D. and Dr. Robert Detrano, M.D., Ph.D., for permitting us to use the heart disease data set for our research work.
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Deshmukh, J., Jangid, M., Gupte, S., Ghosh, S. (2021). Heart Disorder Prognosis Employing KNN, ANN, ID3 and SVM. In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2020. Advances in Intelligent Systems and Computing, vol 1141. Springer, Singapore. https://doi.org/10.1007/978-981-15-3383-9_47
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DOI: https://doi.org/10.1007/978-981-15-3383-9_47
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