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- W2998521300 abstract "In today’s world, business is growing at a very fast pace. The palpable reason for such a fast growth rate is competitive prices and advancement in technology. Introduction of intelligent computational methods like machine learning or deep learning, which is a subset of machine learning, increased the potential of digital procedures and hence every business wants to equip with more and more automated processes for better and early decision-making. Growth in business sector is very important for every nation. One of the business sectors that is growing rapidly in all over the world is the health sector. Health sector is now implementing a number of automated intelligent systems for better decision making. This paper proposed a model based on deep neural network for finding the severity of coronary artery disease (CAD) among patients. For the purpose of experiment, the data has been collected from a medical college under the guidance of doctors. First, the features have been reduced to select only those features, which have a significant effect on the output by using a correlation based feature subset algorithm and then deep learning model is implemented. The proposed model is capable of producing an accuracy of 97.41%." @default.
- W2998521300 created "2020-01-10" @default.
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- W2998521300 date "2020-01-01" @default.
- W2998521300 modified "2023-10-03" @default.
- W2998521300 title "Identification of Severity of Coronary Artery Disease: A Multiclass Deep Learning Framework" @default.
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- W2998521300 doi "https://doi.org/10.1007/978-981-15-0222-4_27" @default.
- W2998521300 hasPublicationYear "2020" @default.
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