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- W3132169797 abstract "Cardiovascular diseases are one of the prime reasons for individual deaths across the globe, claiming millions of lives every year. Cardiovascular disease diagnosis is a critical challenge in the health care field as it has a lot of risk factors associated with it. Moreover, neural networks are ideal in making significant clinical decisions from the huge health-care data produced by the hospitals. This work is an effective method to find significant features and use Deep Neural Networks to build a cardiovascular disease diagnosis system. The proposed system is developed by using a well known dataset called Cleveland dataset of the UCI Repository. The model was introduced with different combinations of features and a deep neural network. The performance of the different subsets of features were evaluated and it was also investigated for a single gender. The proposed system with a minimal feature set helps in the early diagnosis of heart disease and aids the cardiologist in making clinical decisions." @default.
- W3132169797 created "2021-03-01" @default.
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- W3132169797 date "2021-01-01" @default.
- W3132169797 modified "2023-09-23" @default.
- W3132169797 title "A DNN Based Diagnostic System for Heart Disease with Minimal Feature Set" @default.
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- W3132169797 doi "https://doi.org/10.1007/978-981-15-9509-7_5" @default.
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