Abstract
It has been observed that the deaths due to heart disease are increasing day by day, and therefore, the need arises for predicting cardiovascular diseases beforehand. By predicting heart disease in advance, one can start the treatment at an early stage and avoid life-threatening situations. In this research article, models using various classification algorithms are generated, and prediction accuracy is compared. Artificial neural network has the highest prediction accuracy among the other algorithms, viz., K-Nearest Neighbors (KNN), Decision Tree (DT), Support Vector Machine (SVM), and Gaussian Naïve Bayes (GNB), and Artificial Neural Network (ANN) is used for generating models.
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Acknowledgements
We acknowledge UCI Machine Learning Repository for providing datasets related to heart diseases. Further, we also acknowledge Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D., University Hospital, Zurich, Switzerland: William Steinbrunn, M.D., University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D., and V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D., for providing the heart diseases-related data.
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Ramani, P., Pradhan, N., Sharma, A.K. (2020). Classification Algorithms to Predict Heart Diseases—A Survey. In: Gupta, M., Konar, D., Bhattacharyya, S., Biswas, S. (eds) Computer Vision and Machine Intelligence in Medical Image Analysis. Advances in Intelligent Systems and Computing, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-13-8798-2_7
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DOI: https://doi.org/10.1007/978-981-13-8798-2_7
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