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  4. A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48
 
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A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48

Journal
Classification Applications with Deep Learning and Machine Learning Technologies
Studies in Computational Intelligence
ISSN
978-3-031-17576-3
Date Issued
2023
Author(s)
Abu Zitar, Raed 
Physics, Mathematics, Computer science 
Al-Manaseer, Hitham
Abualigah, Laith
Alsoud, Anas Ratib
Ezugwu, Absalom E.
Jia, Heming
DOI
10.1007/978-3-031-17576-3_9
URI
https://depot.sorbonne.ae/handle/20.500.12458/1325
Abstract
In this study, the possibility of using and applying the capabilities of artificial intelligence (AI) and machine learning (ML) to increase the effectiveness of Internet of Things (IoT) and big data in developing a system that supports decision makers in the medical fields was studied. This was done by studying the performance of three well-known classification algorithms Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree-J48 (J48), to predict the probability of heart attack. The performance of the algorithms for accuracy was evaluated using the Healthcare (heart attack possibility) dataset, freely available on kagle. The data was divided into three categories consisting of (303, 909, 1808) instances which were analyzed on the WEKA platform. The results showed that the RFC was the best performer.
Subjects
  • Big data

  • Internet of Things

  • Random forest classif...

  • J48

  • Support vector machin...

  • Weka

  • E-Health

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Acquisition Date
Jan 26, 2023
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