Heart disease prediction with discriminative deep neural network

Yichun Wang

doi:10.1117/12.2673756

22 May 2023 Heart disease prediction with discriminative deep neural network

Yichun Wang

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126401P (2023) https://doi.org/10.1117/12.2673756
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

Heart disease is a serious threat to human health and devastating to families. Sudden heart attacks are often difficult to treat, so it is important to detect and prevent heart disease early. Most of the previous heart disease prediction methods are based on statistical machine learning algorithms, which can achieve good results but can only learn some superficial representations due to their own features, and cannot capture deep relationships, so to solve this problem we use deep neural networks for heart disease prediction. Further, current neural network methods often have difficulty learning discriminative features related to heart disease prediction. Therefore, to solve this problem, we leverage the center loss to enable the neural network to learn discriminative features and separate samples from different categories. We conducted experiments on one dataset, and the experimental results show that our method can effectively improve heart disease prediction while learning more discriminative features.

Citation Download Citation

Yichun Wang "Heart disease prediction with discriminative deep neural network", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126401P (22 May 2023); https://doi.org/10.1117/12.2673756

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KEYWORDS

Cardiovascular disorders

Heart

Machine learning

Neural networks

Neurological disorders

Diseases and disorders

Education and training

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