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A Secure Remote Health Monitoring for Heart Disease Prediction using machine learning and deep learning techniques in XAI framework
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Academic Editor: Francisco Falcone


Machine intelligence models are effective at classifying datasets for data analytics and predicting insights that can be used to make clinical decisions. The models would aid in disease prognosis and preliminary disease investigation, both of which are necessary for effective treatment. In today's world, there is a high demand for the interpretability and explainability of decision models. XAI is an extension of artificial intelligence that extends ai's capability by explaining why it has made that prediction and whether we can rely on it or not. The goal of this paper is to predict heart disease prediction using the RHMIoT model in the XAI framework. The patient clinical data are gathered using medical IoT sensors and stored in a secure cloud storage using a lightweight block encryption and decryption approach. The model is used to predict the accuracy of heart disease and its severity level. The accuracy levels of cardiac disease are calculated using Deep Learning and auto-encoder-based methods. We present a novel strategy for identifying key features using machine learning and deep learning techniques in a secured cloud environment to improve the accuracy of CVD. A lightweight block encryption and decryption technique is provided for a secure RHMIoT. The outcomes were determined using several performance matrices. The performance of auto-encoder Kernel SVM model provided the greatest accuracy of 87.00%. The suggested RHMIoT system identifies the presence of heart disease in a patient and helps to get quick medical attention in case of an emergency situation.

Keywords: Machine learning, Deep Learning ,IoT, XAI,CVD
Comments on this paper
Rasmita Panigrahi
The concept of "A Secure Remote Health Monitoring for Heart Disease Prediction using machine learning and deep learning techniques in XAI framework" encapsulates a forward-thinking and crucial approach to healthcare. The emphasis on security in remote health monitoring is commendable, particularly when dealing with sensitive medical data. The integration of both machine learning and deep learning techniques suggests a nuanced analysis of complex physiological patterns. The mention of an Explainable Artificial Intelligence (XAI) framework is noteworthy, as it addresses the interpretability of the models, making them more transparent and understandable for healthcare professionals and patients. This approach not only contributes to the reliability of predictions but also aligns with ethical considerations and user trust. Overall, the paragraph hints at a holistic solution that not only focuses on prediction accuracy but also on the security, interpretability, and practical application of the proposed remote health monitoring system.

Dear Prof Panigrahi,

Thank you so much for taking the time to read my article and for your incredibly kind words! I'm thrilled to hear that you found it valuable and insightful. Your positive feedback truly means a lot to me and encourages me to continue creating content that resonates with my audience.

I'm delighted that you enjoyed the article and that it provided you with valuable information. Your support and encouragement inspire me to keep striving for excellence in my writing.

Once again, thank you for your encouraging words and for being a part of this journey. I look forward to sharing more content that you will find engaging and beneficial.

Best regards,
Dr. Sibo Prasad Patro.