Context: In the 21st century, the integration of IoT and AI plays a vital role in the real-time monitoring and control of heart disease. As per the records, cardiovascular diseases persist as a significant global health challenge, impacting the lives of over half a billion individuals worldwide.
Objective: The main objective of this paper is to predict heart disease using deep learning techniques.
Materials/Methods: We have considered the performance metrics of deep learning algorithms (Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and Convolutional Neural Networks (CNNs)) achieving accurate and efficient monitoring outcomes through accuracy, precision, recall, and F-measure. We have proposed one model that uses a deep learning algorithm.
Results: Our experimental result reveals that the deep learning algorithm CNN outperforms in comparison to other algorithms and it has achieved 96% accuracy. Another algorithm, ANN, achieved 92% accuracy indicating a balanced precision–recall tradeoff. We further compared our work with the state of the art, and CNN provides a promising result.
Comparison of the proposed work with existing state-of-the-art approaches.
Conclusions: We have collected the IoT sensory data from different patients and integrated them with the machine learning algorithms for real-time monitoring and control for heart disease patients. Our integration approach reveals that CNN is the best classifier that handles multidimensional data
Author Contributions
Conceptualization, N.P. and S.P.P.; methodology, R.P.; software, K.K.S.; validation, N.P. and V.K.S.; formal analysis, N.P.; investigation, R.P.; resources, R.P.; data curation, S.P.P.; writing—original draft preparation, N.P.; writing—review and editing, N.P.; visualization, K.K.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data is available in the Kaggale repository.
Conflicts of Interest
The authors declare no conflict of interest.
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