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AI-Driven cardiac wellness: Predictive modeling for elderly heart health optimization

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Abstract

The integration of the Internet of Things with the Cloud improves our lives by facilitating smooth connections between people and items. Predictive analytics, fueled by cutting-edge machine learning and artificial intelligence, turns reactive healthcare initiatives into proactive ones. A subset of machine learning called deep learning is essential for quickly analyzing large datasets, producing insightful conclusions, and efficiently addressing challenging problems. For early interventions and preventive care, especially for those who are at risk, accurate and timely illness prediction is crucial. Making accurate prediction models becomes crucial when utilizing electronic medical records. Accuracy is improved by using deep learning variations of recurrent neural networks that can handle sequential time-series data. Predictive analytics is applied to cloud-stored electronic medical records and data from Internet of Things devices in this suggested system. With a remarkable accuracy of 98.86%, the smart healthcare system is intended to monitor and anticipate the risk of heart disease utilizing Bi-LSTM (bidirectional long short-term memory). Furthermore, it reaches 98.9% accuracy, 98.8% sensitivity, 98.89% specificity, and 98.86% F-measure. These outcomes greatly surpass the performance of current smart heart disease prediction systems.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity University, Lucknow Campus, Lucknow, Uttar Pradesh, India

    Kamlesh Mani

  2. Department of Electronics and Communication Engineering, Amity University, Lucknow Campus, Lucknow, Uttar Pradesh, India

    Kamlesh Kumar Singh

  3. Medi-Caps University, Indore, Madhya Pradesh, India

    Ratnesh Litoriya

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  1. Kamlesh Mani
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  2. Kamlesh Kumar Singh
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Correspondence to Ratnesh Litoriya.

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Mani, K., Singh, K.K. & Litoriya, R. AI-Driven cardiac wellness: Predictive modeling for elderly heart health optimization. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18453-z

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