An Effective Workflow Scheduling Algorithm in Cloud Computing Using Cat Swarm Optimization

Mangalampalli, Sudheer; Sree, Pokkuluri Kiran; Satish, G Naga; Kumar, K Varada Raj

doi:10.1149/10701.2523ecst

Cited by 4 publications

(1 citation statement)

References 17 publications

(17 reference statements)

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“…In recent years, deep learning, a subset of Artificial Intelligence (AI) that excels at pattern recognition and complex data analysis, has emerged as a promising avenue for transforming healthcare paradigms. This paper delves into the application of deep learning techniques for heart attack prediction, exploring their potential to revolutionize cardiovascular risk assessment [1,2].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Heart Attack Prediction

Kiran Sree

2023

BJSTR

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Cardiovascular diseases, including heart attacks, remain a leading cause of mortality worldwide. Early and accurate prediction of heart attacks is of paramount importance for timely intervention and prevention. Deep learning techniques have shown promising results in various medical applications, and their application in heart attack prediction presents an opportunity to enhance diagnostic capabilities. In this study, we propose a deep learning-based approach for heart attack prediction, leveraging a comprehensive dataset comprising demographic information, medical history, lifestyle factors, and clinical measurements. The dataset is preprocessed to handle missing values, normalize numerical features, and encode categorical variables. Feature selection techniques are employed to identify relevant predictors for heart attack risk. A deep neural network architecture is designed and trained using a subset of the data, with careful consideration given to model interpretability and generalization. The model's performance is evaluated using metrics such as accuracy, precision, recall, and AUC-ROC on a separate test set. The results demonstrate the effectiveness of the proposed deep learning model in predicting heart attacks, showcasing competitive performance compared to traditional methods. Interpretability is enhanced through attention mechanisms, providing insights into the features influencing predictions. The model's deployment is discussed, addressing ethical considerations and compliance with healthcare regulations. The proposed deep learning model holds promise for integration into clinical decision support systems, offering a valuable tool for healthcare professionals in identifying individuals at higher risk of experiencing a heart attack. Further research is encouraged to validate the model on diverse populations and refine its performance in real-world healthcare settings.

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Section: Introductionmentioning

confidence: 99%