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DOI: 10.14569/IJACSA.2019.0100439
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A Hybrid of Multiple Linear Regression Clustering Model with Support Vector Machine for Colorectal Cancer Tumor Size Prediction

Author 1: Muhammad Ammar Shafi
Author 2: Mohd Saifullah Rusiman
Author 3: Shuhaida Ismail
Author 4: Muhamad Ghazali Kamardan

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 4, 2019.

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Abstract: 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.

Keywords: Colorectal cancer; multiple linear regression; support vector machine; fuzzy c- means; clustering; prediction

Muhammad Ammar Shafi, Mohd Saifullah Rusiman, Shuhaida Ismail and Muhamad Ghazali Kamardan, “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(IJACSA), 10(4), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100439

@article{Shafi2019,
title = {A Hybrid of Multiple Linear Regression Clustering Model with Support Vector Machine for Colorectal Cancer Tumor Size Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100439},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100439},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {4},
author = {Muhammad Ammar Shafi and Mohd Saifullah Rusiman and Shuhaida Ismail and Muhamad Ghazali Kamardan}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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