When it comes to the identification of diseases, clinical science is often biased. Several diseases in clinical science can lead to death due to a lack of timely prediction. Among these diseases, cardiac illness is one of them. Approximately one-third of the world population is suffered by cardiac disorder. At present physicians refer different test reports like ECG, and ECO and then use their experience for the detection of cardio vascular disease. But the prediction based on these reports is not always correct. This inaccurate diagnosis mostly results in the demise of the patent. In this research, three types of boosting-based classifiers are applied for the better prediction of cardiac illness. The dataset for the proposed system is the collection of the clinical database of different geographical included in the UCI repository. Among all the proposed classifiers Gradient boosting has shown the best accuracy of 97.62%.
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11 December 2023
MACHINE LEARNING AND INFORMATION PROCESSING: PROCEEDINGS OF ICMLIP 2023
25–26 February 2023
Ranchi, India
Research Article|
December 11 2023
An intelligent cardiac arrest detection using boosting based classifier
Debabrata Swain;
Debabrata Swain
a)
1
Department of Computer Science and Engineering, Pandit Deendayal Energy University
, Gandhinagar, Gujarat, India
a)Corresponding Author: [email protected]
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Devlina Adhikari;
Devlina Adhikari
b)
2
Department of Information and Communication Technology Pandit Deendayal Energy University
, Gandhinagar, Gujarat, India
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Nirmal Keshari Swain;
Nirmal Keshari Swain
c)
3
Department of Information Technology Vardhaman College of Engineering
, Hyderabad, India
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Hargeet Kaur
Hargeet Kaur
d)
1
Department of Computer Science and Engineering, Pandit Deendayal Energy University
, Gandhinagar, Gujarat, India
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a)Corresponding Author: [email protected]
AIP Conf. Proc. 2981, 020023 (2023)
Citation
Debabrata Swain, Devlina Adhikari, Nirmal Keshari Swain, Hargeet Kaur; An intelligent cardiac arrest detection using boosting based classifier. AIP Conf. Proc. 11 December 2023; 2981 (1): 020023. https://doi.org/10.1063/5.0182507
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