Twin support vector machine using kernel function for colorectal cancer detection

Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artiﬁcial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.

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A Melanoma Skin Cancer Detection Using Machine Learning Technique: Support Vector Machine

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1065/1/012039 ◽

2021 ◽

Vol 1065 (1) ◽

pp. 012039

Author(s):

Praveen Banasode ◽

Minal Patil ◽

Nikhil Ammanagi

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Skin Cancer ◽

Cancer Detection ◽

Support Vector ◽

Machine Learning Technique ◽

Learning Technique ◽

Skin Cancer Detection ◽

Melanoma Skin

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Whale optimized mixed kernel function of support vector machine for colorectal cancer diagnosis

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2019.103124 ◽

2019 ◽

Vol 92 ◽

pp. 103124 ◽

Cited By ~ 4

Author(s):

Dandan Zhao ◽

Hong Liu ◽

Yuanjie Zheng ◽

Yanlin He ◽

Dianjie Lu ◽

...

Keyword(s):

Colorectal Cancer ◽

Support Vector Machine ◽

Kernel Function ◽

Cancer Diagnosis ◽

Support Vector

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A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function

Structural Health Monitoring ◽

10.1177/1475921716639587 ◽

2016 ◽

Vol 15 (3) ◽

pp. 302-316 ◽

Cited By ~ 42

Author(s):

Ramin Ghiasi ◽

Peyman Torkzadeh ◽

Mohammad Noori

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Damage Detection ◽

Kernel Function ◽

Structural Damage ◽

Least Square ◽

Support Vector ◽

Learning Approach ◽

Structural Damage Detection ◽

Machine Learning Approach

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Pancreatic Cancer Early Detection Using Twin Support Vector Machine Based on Kernel

Symmetry ◽

10.3390/sym12040667 ◽

2020 ◽

Vol 12 (4) ◽

pp. 667

Author(s):

Wismaji Sadewo ◽

Zuherman Rustam ◽

Hamidah Hamidah ◽

Alifah Roudhoh Chusmarsyah

Keyword(s):

Machine Learning ◽

Pancreatic Cancer ◽

Support Vector Machine ◽

Early Detection ◽

Feature Space ◽

Kernel Functions ◽

Twin Support Vector Machine ◽

Polynomial Kernel ◽

Support Vector ◽

Rbf Kernel

Early detection of pancreatic cancer is difficult, and thus many cases of pancreatic cancer are diagnosed late. When pancreatic cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect pancreatic cancer early. This paper proposes a machine learning approach with the twin support vector machine (TWSVM) method as a new approach to detecting pancreatic cancer early. TWSVM aims to find two symmetry planes such that each plane has a distance close to one data class and as far as possible from another data class. TWSVM is fast in building a model and has good generalizations. However, TWSVM requires kernel functions to operate in the feature space. The kernel functions commonly used are the linear kernel, polynomial kernel, and radial basis function (RBF) kernel. This paper uses the TWSVM method with these kernels and compares the best kernel for use by TWSVM to detect pancreatic cancer early. In this paper, the TWSVM model with each kernel is evaluated using a 10-fold cross validation. The results obtained are that TWSVM based on the kernel is able to detect pancreatic cancer with good performance. However, the best kernel obtained is the RBF kernel, which produces an accuracy of 98%, a sensitivity of 97%, a specificity of 100%, and a running time of around 1.3408 s.

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Prophecy of software maintenance effort with univariate and multivariate approach: By using support vector machine learning techniquewith radial Basis Kernel Function

2017 International Conference on Computing, Communication and Automation (ICCCA) ◽

10.1109/ccaa.2017.8229930 ◽

2017 ◽

Cited By ~ 1

Author(s):

Dimple Chandra ◽

Mehak Choudhary ◽

Deepti Gupta

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Kernel Function ◽

Software Maintenance ◽

Support Vector ◽

Multivariate Approach ◽

Radial Basis

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Least Squares Support Vector Machine Regression Based on Sparse Samples and Mixture Kernel Learning

Information Technology And Control ◽

10.5755/j01.itc.50.2.27752 ◽

2021 ◽

Vol 50 (2) ◽

pp. 319-331

Author(s):

Wenlu Ma ◽

Han Liu

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Least Squares ◽

Kernel Function ◽

Large Scale ◽

Kernel Learning ◽

Selection Strategy ◽

Support Vector ◽

Power Load ◽

Sparse Samples

Least squares support vector machine (LSSVM) is a machine learning algorithm based on statistical theory. Itsadvantages include robustness and calculation simplicity, and it has good performance in the data processingof small samples. The LSSVM model lacks sparsity and is unable to handle large-scale data problem, this articleproposes an LSSVM method based on mixture kernel learning and sparse samples. This algorithm reduces theinitial training set to a sub-dataset using a sparse selection strategy. It converts the single kernel function in theLSSVM model into a mixed kernel function and optimizes its parameters. The reduced sub-dataset is used fortraining LSSVM. Finally, a group of datasets in the UCI Machine Learning Repository were used to verify theeffectiveness of the proposed algorithm, which is applied to real-world power load data to achieve better fittingand improve the prediction accuracy.

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Early Cancer Detection from Genome-wide Cell-free DNA Fragmentation via Shuffled Frog Leaping Algorithm and Support Vector Machine

Bioinformatics ◽

10.1093/bioinformatics/btab236 ◽

2021 ◽

Author(s):

Linjing Liu ◽

Xingjian Chen ◽

Ka-Chun Wong

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Cancer Detection ◽

Early Cancer ◽

Machine Learning Algorithms ◽

Early Cancer Detection ◽

Supplementary Information ◽

Support Vector ◽

Shuffled Frog Leaping Algorithm ◽

Shuffled Frog Leaping

Abstract Motivation Early cancer detection is significant for the patient mortality rate reduction. Although machine learning has been widely employed in that context, there are still deficiencies. In this work, we studied different machine learning algorithms for early cancer detection and proposed an Adaptive Support Vector Machine (ASVM) method by synergizing Shuffled Frog Leaping Algorithm (SFLA) and Support Vector Machine (SVM) in this paper. Results As ASVM regulates SVM for parameter adaption based on data characteristics, the experimental results demonstrated the robust generalization capability of ASVM on different datasets under different settings; for instance, ASVM can enhance the sensitivity by over 10% for early cancer detection compared with SVM. Besides, our proposed ASVM outperformed Grid Search + SVM and Random Search + SVM by significant margins in terms of the area under the ROC curve (AUC) (0.938 vs. 0.922 vs. 0.921). Availability The proposed algorithm and dataset are available at https://github.com/ElaineLIU-920/ASVM-for-Early-Cancer-Detection. Supplementary information Supplementary data are available at Bioinformatics online.

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