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- W4386984037 abstract "Every year, heart disease kills more people than any other single disease combined. Heart disease will be the primary cause of death for more than 21% of people worldwide in 2020, according to the WHO. Heart pumps the oxygen-rich blood throughout the body, in order to keep the organs functional. Cardiomyopathy and cardiovascular disease (CVD) all fall under the umbrella term ‘heart stroke’ indicating the blockage of blood flow across the cardiovascular system (CVD). It is possible to forecast the prevalence of heart stroke by analysing medical data that is based on specialists’ clinical competence. Patients who suffer a heart attack can be helped by early detection and treatment of cardiac abnormalities, which can help doctors better understand the underlying causes of the condition. Researchers utilized a deep learning model trained on data from heart disease patients to estimate the risk of a stroke. Atrial fibrillation, a key risk factor for stroke, is more common in people with heart disease. The suggested strategy gives more dependable methodologies for performance evaluation. Using convolutional neural networks (CNN) as a deep learning technique, an efficient Interconnected Feature Labelled Model using CNN (IFLM-CNN) approach is proposed in this paper for accurate prediction of heart stroke. Our proposed model predicts early heart stroke symptoms and helps physicians in treatment. The proposed model when contrasted to existing model exhibits better prediction results." @default.
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- W4386984037 date "2023-01-01" @default.
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- W4386984037 title "Prediction of Heart Stroke Using Improved Feature Extraction Based CNN Model" @default.
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- W4386984037 doi "https://doi.org/10.1007/978-981-99-1431-9_52" @default.
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