A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48
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...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://depot.sorbonne.ae/handle/20.500.12458/1325 |
| الوسوم: |
إضافة وسم
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| _version_ | 1857415064845287424 |
|---|---|
| author | Al-Manaseer, Hitham |
| author2 | Abualigah, Laith Alsoud, Anas Ratib Abu Zitar, Raed Ezugwu, Absalom E. Jia, Heming |
| author2_role | author author author author author |
| author_facet | Al-Manaseer, Hitham Abualigah, Laith Alsoud, Anas Ratib Abu Zitar, Raed Ezugwu, Absalom E. Jia, Heming |
| author_role | author |
| dc.creator.none.fl_str_mv | Al-Manaseer, Hitham Abualigah, Laith Alsoud, Anas Ratib Abu Zitar, Raed Ezugwu, Absalom E. Jia, Heming |
| dc.date.none.fl_str_mv | 2022-11-21T04:48:42Z 2022-11-21T04:48:42Z 2023 |
| dc.identifier.none.fl_str_mv | 9783031175763 1860-949X 1860-9503 https://depot.sorbonne.ae/handle/20.500.12458/1325 10.1007/978-3-031-17576-3_9 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | Studies in Computational Intelligence Studies in Computational Intelligence 978-3-031-17576-3 |
| dc.subject.none.fl_str_mv | Big data Internet of Things Random forest classifier J48 Support vector machine Weka E-Health |
| dc.title.none.fl_str_mv | A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48 |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::book::book part |
| description | 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. |
| id | sorbonner_ff3ba8d1f8c0eb67ab844cd0de54f73a |
| identifier_str_mv | 9783031175763 1860-949X 1860-9503 10.1007/978-3-031-17576-3_9 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1325 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48Al-Manaseer, HithamAbualigah, LaithAlsoud, Anas RatibAbu Zitar, RaedEzugwu, Absalom E.Jia, HemingBig dataInternet of ThingsRandom forest classifierJ48Support vector machineWekaE-HealthIn 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.2022-11-21T04:48:42Z2022-11-21T04:48:42Z2023Controlled Vocabulary for Resource Type Genres::text::book::book part97830311757631860-949X1860-9503https://depot.sorbonne.ae/handle/20.500.12458/132510.1007/978-3-031-17576-3_9enStudies in Computational IntelligenceStudies in Computational Intelligence978-3-031-17576-3oai:depot.sorbonne.ae:20.500.12458/13252024-03-10T08:11:55Z |
| spellingShingle | A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48 Al-Manaseer, Hitham Big data Internet of Things Random forest classifier J48 Support vector machine Weka E-Health |
| title | A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48 |
| title_full | A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48 |
| title_fullStr | A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48 |
| title_full_unstemmed | A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48 |
| title_short | A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48 |
| title_sort | A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48 |
| topic | Big data Internet of Things Random forest classifier J48 Support vector machine Weka E-Health |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1325 |