期刊名称：International Journal of Early Childhood Special Education
电子版ISSN：1308-5581
出版年度：2022
卷号：14
期号：2
页码：5811-5822
DOI：10.9756/INT-JECSE/V14I2.654
语种：English
出版社：International Journal of Early Childhood Special Education
摘要：Background: According to the Indian Heart Association, by 2030 the mortality rate due to heart disease especially in coma patients are likely to be increase by 2.3 crore. Therefore there is demand to adapt the new intelligent techniques to help these patients. Methods: In our proposed system the Monetization of the Coma Patient (MCP) and prediction of the Heart attack of a patient is going to be forecasted. We used used the hybrid machine learning techniques namely the clustering algorithm (K-Means and classifications (Naive Bayes, SVM, Voted Perceptron and Naive Bayes) algorithms to evaluate and recognize the best appropriate model. Findings: The accuracy of algorithms (89.3%, 65.83%, and 59.83%) of the above machine learning techniques were analyzed and compared respectively. The performance of Naive Bayes is the best among the other models, hence Naive Bayes is the most efficient model. Novelty and Applications: The hybrid system is built using Naive Bayes and k-means, which improves the response time along with accuracy for MCP and prediction of the Heart attack. Conclusion and Future Scope: The machine learning algorithms such as Decision tree, logistic regression models or artificial neural networks combining multiple algorithms may also help to increase the accuracy of the tool against the models presented in this study. On the whole, IOT and Machine learning adds a new dimension to health and patient care with remote monitor and predicting heart attack.
关键词：Internet of things;Machine learning;Coma patients;Heart attack;Naive Bayes
