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Breast cancer prediction model with decision tree and adaptive boosting

IAES International Journal of Artificial Intelligence · Vol. 10 No. 1 (2021) · hlm. 184–190

DOI
10.11591/ijai.v10.i1.pp184-190
ISSN
2252-8938, 2089-4872
Terbit
2021-03-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.

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  • St. Peter's Institute of Higher Education and Research
  • Koneru Lakshmaiah Education Foundation
  • Injibara University
  • Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology
  • University of Port Harcourt
  • University of Nigeria
  • R.V. College of Engineering
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Rekan penulisOrganisasiArtikel bersama
Tamilarasi SureshSt. Peter's Institute of Higher Education and Research8
Ayodeji Olalekan SalauAfe Babalola University6
D. VigneswariKCG College of Technology5
Sangeetha GanesanAfiliasi tidak diketahui4
Angati Kalyan KumarAfiliasi tidak diketahui4
Radha MothukuriKoneru Lakshmaiah Education Foundation3
Rajkumar GovindarajanAfiliasi tidak diketahui3
Raguraman PurushothamanAfiliasi tidak diketahui3
Janakiraman Senthil MuruganRajalakshmi Engineering College2
Billa ManindharKoneru Lakshmaiah Education Foundation2
R. SubhashniSt. Peter's Institute of Higher Education and Research2
Amogne Andualem AyalewMizan Tepi University2
Melaku Lake TegegneInjibara University2
Bommy ManivannanAfiliasi tidak diketahui2
Battula Krishna PrasadKoneru Lakshmaiah Education Foundation2
Maran Jeyanthiran Thiruvarasu Vasantha PriyaAfiliasi tidak diketahui2
Thiyagu ThulasiAfiliasi tidak diketahui2
R Prasanna KumarAmrita Vishwa Vidyapeetham2
Sangeetha MuruganAfiliasi tidak diketahui2
Kamaluru MahammadAfiliasi tidak diketahui2
Jayanthi ArumugamAnna University, Chennai2
Komal Kumar NVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology1
Pramod Sekharan NairAksum University1
Crescent Onyebuchi OmejeUniversity of Port Harcourt1
Sepiribo Lucky BraideUniversity of Port Harcourt1
Elisha Didam MarkusCentral University of Technology1
Joy Nnenna EnehUniversity of Nigeria1
Thankgod Izuchukwu OzueUniversity of Nigeria1
Sathya SubramanianAfiliasi tidak diketahui1
Durga Devi SaravananAfiliasi tidak diketahui1
Lokeshwaran KanagarajVel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology1
Cornelius KarunakaranAfiliasi tidak diketahui1
Vidhyashree BalajiAfiliasi tidak diketahui1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Breast cancer prediction model with decision tree and adaptive boosting</dc:title>
	<dc:creator>Assegie, Tsehay Admassu</dc:creator>
	<dc:creator>Tulasi, R. Lakshmi</dc:creator>
	<dc:creator>Kumar, N. Komal</dc:creator>
	<dc:subject xml:lang="en-US">Machine learning, classification algorithms</dc:subject>
	<dc:subject xml:lang="en-US">Adaboost; Breast cancer; Breast cancer prediction; Decision tree; Machine learning</dc:subject>
	<dc:description xml:lang="en-US">In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/20395</dc:identifier>
	<dc:identifier>10.11591/ijai.v10.i1.pp184-190</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 10, No 1: March 2021; 184-190</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v10.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20395/13075</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>