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• W3200364249 abstract "Heart disease is definitely among the many most significant triggers of morbidity and fatality amid the populace among the globe. Prediction of cardiac disease can be considered as one particular among the most crucial topics in the sector of medical info evaluation. The quantity of data through the medical industry is very large. Deep learning becomes the huge range of natural medical care data straight to data which usually may support to identify possibilities and forecasts. This paper reveals the novel algorithm and performance methodology that can forecast the heart disease by ways of CNN modeling. The parameters evaluation will be done for accuracy, sensitivity, specificity, and positive prediction value (PPV). Such parameters can be used in a user-friendly manner by doctors to trace out the possibility of diseases." @default.
• W3200364249 created "2021-09-27" @default.
• W3200364249 creator A5082037255 @default.
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• W3200364249 date "2021-09-20" @default.
• W3200364249 modified "2023-10-01" @default.
• W3200364249 title "Addressing Concept Drifts Using Deep Learning for Heart Disease Prediction: A Review" @default.
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• W3200364249 doi "https://doi.org/10.1007/978-981-16-3346-1_13" @default.
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