Cover Image

A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction

This study proposed the new hybrid model of Multiple Linear Regression Clustering (MLRC) combined with Support Vector Machine (SVM) to predict tumor size of colorectal cancer (CRC). Three models: Multiple Linear Regression (MLR), MLRC and hybrid MLRC with SVM model were compared to get the best mode...

Full description

Saved in:
Bibliographic Details
Main Authors: Shafi, Muhammad Ammar, Rusiman, Mohd Saifullah, Ismail, Shuhaida, Kamardan, Muhamad Ghazali
Format: Article
Language:English
Published: SAI Organization 2019
Subjects:
QA273-280 Probabilities. Mathematical statistics
Online Access:http://eprints.uthm.edu.my/2296/1/AJ%202019%20%289%29.pdf
http://eprints.uthm.edu.my/2296/
http://dx.doi.org/10.14569/IJACSA.2019.0100439
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uthm.eprints.2296
record_format eprints
spelling my.uthm.eprints.22962021-10-18T08:04:28Z http://eprints.uthm.edu.my/2296/ A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction Shafi, Muhammad Ammar Rusiman, Mohd Saifullah Ismail, Shuhaida Kamardan, Muhamad Ghazali QA273-280 Probabilities. Mathematical statistics This study proposed the new hybrid model of Multiple Linear Regression Clustering (MLRC) combined with Support Vector Machine (SVM) to predict tumor size of colorectal cancer (CRC). Three models: Multiple Linear Regression (MLR), MLRC and hybrid MLRC with SVM model were compared to get the best model in predicting tumor size of colorectal cancer using two measurement statistical errors. The proposed model of hybrid MLRC with SVM have found two significant clusters whereby, each clusters contained 15 and three significant variables for cluster 1 and 2, respectively. The experiments found that the proposed model tend to be the best model with least value of Mean Square Error (MSE) and Root Mean Square Error (RMSE). This finding has shed light to health practitioner in determining the factors that contribute to colorectal cancer. SAI Organization 2019 Article PeerReviewed text en http://eprints.uthm.edu.my/2296/1/AJ%202019%20%289%29.pdf Shafi, Muhammad Ammar and Rusiman, Mohd Saifullah and Ismail, Shuhaida and Kamardan, Muhamad Ghazali (2019) A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction. International Journal of Advanced Computer Science and Applications, 10 (4). pp. 323-328. ISSN 2158-107X http://dx.doi.org/10.14569/IJACSA.2019.0100439
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic QA273-280 Probabilities. Mathematical statistics
spellingShingle QA273-280 Probabilities. Mathematical statistics
Shafi, Muhammad Ammar
Rusiman, Mohd Saifullah
Ismail, Shuhaida
Kamardan, Muhamad Ghazali
A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction
description This study proposed the new hybrid model of Multiple Linear Regression Clustering (MLRC) combined with Support Vector Machine (SVM) to predict tumor size of colorectal cancer (CRC). Three models: Multiple Linear Regression (MLR), MLRC and hybrid MLRC with SVM model were compared to get the best model in predicting tumor size of colorectal cancer using two measurement statistical errors. The proposed model of hybrid MLRC with SVM have found two significant clusters whereby, each clusters contained 15 and three significant variables for cluster 1 and 2, respectively. The experiments found that the proposed model tend to be the best model with least value of Mean Square Error (MSE) and Root Mean Square Error (RMSE). This finding has shed light to health practitioner in determining the factors that contribute to colorectal cancer.
format Article
author Shafi, Muhammad Ammar
Rusiman, Mohd Saifullah
Ismail, Shuhaida
Kamardan, Muhamad Ghazali
author_facet Shafi, Muhammad Ammar
Rusiman, Mohd Saifullah
Ismail, Shuhaida
Kamardan, Muhamad Ghazali
author_sort Shafi, Muhammad Ammar
title A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction
title_short A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction
title_full A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction
title_fullStr A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction
title_full_unstemmed A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction
title_sort hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction
publisher SAI Organization
publishDate 2019
url http://eprints.uthm.edu.my/2296/1/AJ%202019%20%289%29.pdf
http://eprints.uthm.edu.my/2296/
http://dx.doi.org/10.14569/IJACSA.2019.0100439
_version_ 1738580972096454656
score 13.160551

Similar Items