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- W4386014782 abstract "Heart disorders are leading cause of deaths worldwide. If in case a risk of heart disease may include for the patient, the healthcare department people are mostly depending on the data of a patient. The data cannot be verified in-detail by the doctors all the time and predict accurately. However, it is concerning as the risky and time consuming as well. This paper aims at developing a deep learning-based heart disease prediction system that can be used to diagnose a patient’s condition based on the medical record. Several parameters like fasting blood sugar, cholesterol, chest pain type, and resting blood pressure have been studied and used for the detection of heart disease. The parameters have been fed to a 1D convolutional neural network (CNN). The network is designed with convolution 2D layer, ReLU layer, softmax layer, fully-connected layer, and classification layer. The UCI heart dataset has been used for the experiments. The proposed system has produced an accuracy of 90% during training and 82% during testing." @default.
- W4386014782 created "2023-08-21" @default.
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- W4386014782 date "2023-01-01" @default.
- W4386014782 modified "2023-09-27" @default.
- W4386014782 title "Heart Disorder Prediction Using 1D Convolutional Neural Network" @default.
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- W4386014782 doi "https://doi.org/10.1007/978-981-99-3691-5_44" @default.
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