Abstract
Cardiovascular heart diseases are most important demise of life. The early prediction of cardiovascular disease of heart is challenging task in the clinical healthcare. Machine learning is part of artificial intelligence. In this, we have considered some feature which can lead to illness of heart. An enhanced technique of machine learning to predict heart disease using different features has been proposed. The aim is to detect best classification algorithm for disease prediction with maximum accuracy. We have taken datasets from UCI ML dataset with 1025 instances and 14 attributes. We have applied three different ML algorithms such as, Naïve Bayes, K-nearest neighbor, and decision tree. We proposed decision tree technique with 100% accuracy when compared to Naïve Bayes and K-NN, and moreover, time taken to build model is less when compared to other algorithm. The second place is taken by Naïve Bayes algorithm with 86.38% accuracy. Third place is taken by K-NN which has given 85.99% accuracy. This result will benefit to select the best classification algorithm for heart disease prediction and can be used for detection and treatment.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Shilpa, K., Adilakshmi, T. (2023). An Enhanced Machine Learning Technique to Predict Heart Disease. In: Kumar, A., Ghinea, G., Merugu, S. (eds) Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. ICCIC 2022. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-2742-5_19
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DOI: https://doi.org/10.1007/978-981-99-2742-5_19
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