Breast cancer is one of the most hazardous of all types of cancer affecting mainly women. It is the second leading cause of death in Nigerian women. It is difficult to classify breast tumour. The diagnosis of breast cancer on patients in hospitals and clinics is highly subjective and it is reliant on the physician’s expertise. This may often lead to incorrect diagnosis and long waiting time to diagnose breast tumour which may increase the possibility of Cancer metastasizing. This study focused on developing a multi-agent based model for diagnosis of breast tumours using the k-Nearest Neighbor (k-NN) algorithm by classifying the nature of the tumours based on their associated patterns of symptoms and other risk factors of Cancer diseases. A k-NN algorithm using Java and MYSQ was developed to extract and classify the symptoms associated with Breast Cancer. Java Agent Development Environment (JADE) was used for the modeling and simulation. The accuracy score was tested on a breast tumour clinical datasets which were gotten and formed from Federal Medical Centers (FMC) Yola and Gombe in Nigeria. The experimental result of the prediction model shows a percentage accuracy score of 98.9%.
| Published in | American Journal of Data Mining and Knowledge Discovery (Volume 4, Issue 1) |
| DOI | 10.11648/j.ajdmkd.20190401.11 |
| Page(s) | 1-7 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2019. Published by Science Publishing Group |
Breast Tumour, Multi-Agent, k-NN Algorithm
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APA Style
Yusuf Musa Malgwi, Gregory Maksha Wajiga, Etemi Joshua Garba. (2019). Multi-Agent Based Diagnostic Model for Breast Tumour Classification. American Journal of Data Mining and Knowledge Discovery, 4(1), 1-7. https://doi.org/10.11648/j.ajdmkd.20190401.11
ACS Style
Yusuf Musa Malgwi; Gregory Maksha Wajiga; Etemi Joshua Garba. Multi-Agent Based Diagnostic Model for Breast Tumour Classification. Am. J. Data Min. Knowl. Discov. 2019, 4(1), 1-7. doi: 10.11648/j.ajdmkd.20190401.11
AMA Style
Yusuf Musa Malgwi, Gregory Maksha Wajiga, Etemi Joshua Garba. Multi-Agent Based Diagnostic Model for Breast Tumour Classification. Am J Data Min Knowl Discov. 2019;4(1):1-7. doi: 10.11648/j.ajdmkd.20190401.11
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Download@article{10.11648/j.ajdmkd.20190401.11,
author = {Yusuf Musa Malgwi and Gregory Maksha Wajiga and Etemi Joshua Garba},
title = {Multi-Agent Based Diagnostic Model for Breast Tumour Classification},
journal = {American Journal of Data Mining and Knowledge Discovery},
volume = {4},
number = {1},
pages = {1-7},
doi = {10.11648/j.ajdmkd.20190401.11},
url = {https://doi.org/10.11648/j.ajdmkd.20190401.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20190401.11},
abstract = {Breast cancer is one of the most hazardous of all types of cancer affecting mainly women. It is the second leading cause of death in Nigerian women. It is difficult to classify breast tumour. The diagnosis of breast cancer on patients in hospitals and clinics is highly subjective and it is reliant on the physician’s expertise. This may often lead to incorrect diagnosis and long waiting time to diagnose breast tumour which may increase the possibility of Cancer metastasizing. This study focused on developing a multi-agent based model for diagnosis of breast tumours using the k-Nearest Neighbor (k-NN) algorithm by classifying the nature of the tumours based on their associated patterns of symptoms and other risk factors of Cancer diseases. A k-NN algorithm using Java and MYSQ was developed to extract and classify the symptoms associated with Breast Cancer. Java Agent Development Environment (JADE) was used for the modeling and simulation. The accuracy score was tested on a breast tumour clinical datasets which were gotten and formed from Federal Medical Centers (FMC) Yola and Gombe in Nigeria. The experimental result of the prediction model shows a percentage accuracy score of 98.9%.},
year = {2019}
}
TY - JOUR T1 - Multi-Agent Based Diagnostic Model for Breast Tumour Classification AU - Yusuf Musa Malgwi AU - Gregory Maksha Wajiga AU - Etemi Joshua Garba Y1 - 2019/04/10 PY - 2019 N1 - https://doi.org/10.11648/j.ajdmkd.20190401.11 DO - 10.11648/j.ajdmkd.20190401.11 T2 - American Journal of Data Mining and Knowledge Discovery JF - American Journal of Data Mining and Knowledge Discovery JO - American Journal of Data Mining and Knowledge Discovery SP - 1 EP - 7 PB - Science Publishing Group SN - 2578-7837 UR - https://doi.org/10.11648/j.ajdmkd.20190401.11 AB - Breast cancer is one of the most hazardous of all types of cancer affecting mainly women. It is the second leading cause of death in Nigerian women. It is difficult to classify breast tumour. The diagnosis of breast cancer on patients in hospitals and clinics is highly subjective and it is reliant on the physician’s expertise. This may often lead to incorrect diagnosis and long waiting time to diagnose breast tumour which may increase the possibility of Cancer metastasizing. This study focused on developing a multi-agent based model for diagnosis of breast tumours using the k-Nearest Neighbor (k-NN) algorithm by classifying the nature of the tumours based on their associated patterns of symptoms and other risk factors of Cancer diseases. A k-NN algorithm using Java and MYSQ was developed to extract and classify the symptoms associated with Breast Cancer. Java Agent Development Environment (JADE) was used for the modeling and simulation. The accuracy score was tested on a breast tumour clinical datasets which were gotten and formed from Federal Medical Centers (FMC) Yola and Gombe in Nigeria. The experimental result of the prediction model shows a percentage accuracy score of 98.9%. VL - 4 IS - 1 ER -
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