A Hybrid of Multiple Linear Regression Clustering Model with Support Vector Machine for Colorectal Cancer Tumor Size Prediction

被引:0
|
作者
Shafi, Muhammad Ammar [1 ]
Rusiman, Mohd Saifullah [1 ]
Ismail, Shuhaida [1 ]
Kamardan, Muhamad Ghazali [1 ]
机构
[1] Univ Tun Hussein Onn Malaysia, Dept Math & Stat, Pagoh Muar 86400, Johor, Malaysia
关键词
Colorectal cancer; multiple linear regression; support vector machine; fuzzy c- means; clustering; prediction;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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.
引用
收藏
页码:323 / 328
页数:6
相关论文
共 50 条
  • [21] FUZZY CLUSTERING MULTIPLE KERNEL SUPPORT VECTOR MACHINE
    Cheng, Gong
    Tong, Xiaojun
    PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION (ICWAPR), 2018, : 7 - 12
  • [22] A hybrid Support Vector Regression for Time Series Prediction
    Li, Qiong
    Fu, Yuchen
    Zhou, Xiaoke
    Xu, Yunlong
    THIRD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING: WKDD 2010, PROCEEDINGS, 2010, : 506 - 509
  • [23] PREDICTION OF SOLAR FLARE SIZE AND TIME-TO-FLARE USING SUPPORT VECTOR MACHINE REGRESSION
    Boucheron, Laura E.
    Al-Ghraibah, Amani
    McAteer, R. T. James
    ASTROPHYSICAL JOURNAL, 2015, 812 (01):
  • [24] Prediction of dielectric dissipation factors of polymers from cyclic dimer structure using multiple linear regression and support vector machine
    Xu, Jie
    Zhu, Ligen
    Fang, Dong
    Liu, Li
    Wang, Luoxin
    Xu, Weilin
    COLLOID AND POLYMER SCIENCE, 2013, 291 (03) : 551 - 561
  • [25] Prediction of dielectric dissipation factors of polymers from cyclic dimer structure using multiple linear regression and support vector machine
    Jie Xu
    Ligen Zhu
    Dong Fang
    Li Liu
    Luoxin Wang
    Weilin Xu
    Colloid and Polymer Science, 2013, 291 : 551 - 561
  • [26] Short-Term Wind Power Prediction Based on DBSCAN Clustering and Support Vector Machine Regression
    Wang, Siqi
    Chen, Chen
    2020 5TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION SYSTEMS (ICCCS 2020), 2020, : 941 - 945
  • [27] An Improved Hybrid ARIMA and Support Vector Machine Model for Water Quality Prediction
    Guo, Yishuai
    Wang, Guoyin
    Zhang, Xuerui
    Deng, Weihui
    ROUGH SETS AND KNOWLEDGE TECHNOLOGY, RSKT 2014, 2014, 8818 : 411 - 422
  • [28] Research on the Cost Prediction Model of Construction Projects Based on the Support Vector Regression Machine
    Kong, Xiangpeng
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2020, 126 : 284 - 284
  • [29] Improved Hybrid Model Based on Support Vector Regression Machine for Monthly Precipitation Forecasting
    Chen, Xuejun
    Zhu, Suling
    JOURNAL OF COMPUTERS, 2013, 8 (01) : 232 - 239
  • [30] CARBON EMISSION PREDICTION MODEL OF AGROFORESTRY ECOSYSTEM BASED ON SUPPORT VECTOR REGRESSION MACHINE
    Cai, J.
    Ma, X.
    APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH, 2019, 17 (03): : 6397 - 6413