scholarly journals Performance of Support Vector Machine Kernels (SVM-K) on Breast Cancer (BC) Dataset

2019 ◽  
Vol 8 (2S7) ◽  
pp. 412-417
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Basis Function ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Radial Basis Function Kernel ◽  
Linear Kernel ◽  
Real Situation ◽  
Preventive Policy

Breast cancer (BC) most diagnosed invasive disorder and important cause of casualty for women worldwide. Indian contest BC most commonly spread disease among females. This problem is more alarming to economically developing country like India. Government of India made a lot of effort to make aware the women of the country, but despite of availability of diagnostic tool, prediction of disease in real situation is still a puzzle for researchers. Timely detection and categorization of BC using the evolving techniques like Machine Learning (ML) can show a significant role in BC identification and this could be a preventive policy which effectively reduces the risk of BC patients. Although there are four Kernels in ML, are widely in use but their performance varies with the kind of data available. In this study we, apply four different Kernels such as Linear Kernel (LK), Polynomial Kernel (PK), Sigmoid Kernel (SK) and Radial Basis Function Kernel (RBFK) on BC dataset. We estimated the performance of Support Vector Machine Kernels (SVM-K) on BC dataset .The basic idea is to check the exactness of SVM-K to classify WBCD in terms of effectiveness with respect to accuracy, runtime, specificity and precision. The investigations outcome displays that RBFK provides greater accuracy with minimal errors

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

Symmetry ◽  
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.

scholarly journals Modeling Student’s Academic Performance During Covid-19 Based on Classification in Support Vector Machine

2021 ◽  
Vol 12 (5) ◽  
pp. 1798-1804
Author(s):  
Nor Ain Maisarah Samsudin, Et. al.
Keyword(s):  
Support Vector Machine ◽  
Academic Performance ◽  
Undergraduate Students ◽  
Movement Control ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Linear Kernel ◽  
Grade Point ◽  
Before And After ◽  
And Mathematics

This study proposed a statistical investigate the pattern of students’ academic performance before and after online learning due to the Movement Control Order (MCO) during pandemic outbreak and a modelling students’ academic performance based on classification in Support Vector Machine (SVM). Data sample were taken from undergraduate students of Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris (UPSI). Student’s Grade Point Average (GPA) were obtained to developed model of academic performances during Covid-19 outbreak. The prediction model was used to predict the academic performances of university students when online classes was conducted. The algorithm of Support Vector Machine (SVM) was used to develop a model of students’ academic performance in university. For the Support Vector Machine (SVM) algorithm, there are two important parameters which are C (misclassification tolerance parameter) and epsilon  need to identify before proceed the further analysis. The parameters was applied to four different types of kernel which is linear kernel, radial basis function kernel, polynomial kernel and sigmoid kernel and the result was found that the best accuracy achieved by SVM are 73.68% by using linear kernel and the worst accuracy obtained from a sigmoid kernel which is 67.99% with parameter of misclassification tolerance C is 128 and epsilon is 0.6.

Prediction of Breast Cancer Using Machine Learning

2020 ◽  
Vol 13 (5) ◽  
pp. 901-908
Author(s):  
Somil Jain ◽  
Puneet Kumar
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Random Forest ◽  
Prediction Accuracy ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Algorithms ◽  
Breast Cancer Dataset

Background:: Breast cancer is one of the diseases which cause number of deaths ever year across the globe, early detection and diagnosis of such type of disease is a challenging task in order to reduce the number of deaths. Now a days various techniques of machine learning and data mining are used for medical diagnosis which has proven there metal by which prediction can be done for the chronic diseases like cancer which can save the life’s of the patients suffering from such type of disease. The major concern of this study is to find the prediction accuracy of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest and to suggest the best algorithm. Objective:: The objective of this study is to assess the prediction accuracy of the classification algorithms in terms of efficiency and effectiveness. Methods: This paper provides a detailed analysis of the classification algorithms like Support Vector Machine, J48, Naïve Bayes and Random Forest in terms of their prediction accuracy by applying 10 fold cross validation technique on the Wisconsin Diagnostic Breast Cancer dataset using WEKA open source tool. Results:: The result of this study states that Support Vector Machine has achieved the highest prediction accuracy of 97.89 % with low error rate of 0.14%. Conclusion:: This paper provides a clear view over the performance of the classification algorithms in terms of their predicting ability which provides a helping hand to the medical practitioners to diagnose the chronic disease like breast cancer effectively.

scholarly journals Using Support Vector Machine Detection of Breast Cancer in Early stage

2020 ◽  
pp. 213-216
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Sensitivity Specificity

The Breast Cancer is disease which tremendously increased in women’s nowadays. Mammography is technique of low-powered X-ray diagnosis approach for detection and diagnosis of cancer diseases at early stage. The proposed system shows the solution of two problems. First shows to detect tumors as suspicious regions with a weak contrast to their background and second shows way to extract features which categorize tumors. Hence this classification can be done with SVM, a great method of statistical learning has made significant achievement in various field. Discovered in the early 90’s, which led to an interest in machine learning? Here the different types of tumor like Benign, Malignant, or Normal image are classified using the SVM classifier. This techniques shows how easily we can detect region of tumor is present in mammogram images with more than 80% of accuracy rates for linear classification using SVM. The 10-fold cross validation to get an accurate outcome is been used by proposed system. The Wisconsin breast cancer diagnosis data set is referred from UCI machine learning repository. The considering accuracy, sensitivity, specificity, false discovery rate, false omission rate and Matthews’s correlation coefficient is appraised in the proposed system. This Provides good result for both training and testing phase. The techniques also shows accuracy of 98.57% and 97.14% by use of Support Vector Machine and K-Nearest Neighbors

scholarly journals BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm

2021 ◽  
Vol 7 ◽  
pp. e390
Author(s):  
Shafaq Abbas ◽  
Zunera Jalil ◽  
Abdul Rehman Javed ◽  
Iqra Batool ◽  
Mohammad Zubair Khan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Experimental Results ◽  
Support Vector ◽  
Novel Approach ◽  
Whale Optimization

Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.

Detection of Breast Cancer Using Machine Learning Support Vector Machine Algorithm

2019 ◽  
Vol 16 (2) ◽  
pp. 441-444
Author(s):  
D. V. Soundari ◽  
R. Padmapriya ◽  
C. Thirumariselvi ◽  
N. Nanthini ◽  
K. Priyadharsini
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Learning Support ◽  
Support Vector Machine Algorithm ◽  
Breast Cancer Data ◽  
Data Set ◽  
Cancer Data ◽  
Hormone Imbalance

A woman majorly suffers due to breast cancer which is due to hormone imbalance. It leads to huge death in recent years. Early detection of the breast cancer is more important to prevent human lives. Image Processing plays an important to classify and detect the same. So this paper proposes machine learning based cancer classification using support vector machine with Wisconsin breast cancer data set.

Adaptive data-driven nonlinear synchro squeezed transform with single class radial basis function kernel support vector machine applied to wind turbine planetary gearbox fault diagnostics

2019 ◽  
Vol 234 (7) ◽  
pp. 1015-1025
Author(s):  
R Uma Maheswari ◽  
R Umamaheswari
Keyword(s):  
Support Vector Machine ◽  
High Resolution ◽  
Radial Basis Function ◽  
Amplitude Modulation ◽  
Basis Function ◽  
Support Vector ◽  
Radial Basis Function Kernel ◽  
Single Class ◽  
Kernel Support Vector Machine ◽  
Radial Basis

Planetary stage gears operated at low rotational speed and varying wind speed result variation in load. Variable speed and variable load induce nonstationary operating conditions. Vibration signal measured from Wind power gear transmission systems are embedded with multiple sources of vibration and attenuated considerably as it travels from source of vibration to measuring point. Efficacious multi-component decomposition without mode mixing ensures the accurate fault signature recognition. Synchro squeezing transform is the promising tool that represents the ridges with high resolution in time as well as in frequency axis. An efficient vibration analysis technique, short windowed Fourier synchro squeezing transform with nonlinear radial basis function kernel support vector machine is proposed to detect the mechanical faults in low speed planetary stage of wind turbines. Raw vibration is modeled in time–frequency plane to extract fault pattern signatures effectively with high resolution by adapting an empirical nonlinear tool synchro squeezing transforms. Amplitude modulation and frequency modulation parameters are sculpted from instantaneous amplitude and instantaneous phase, frequency. Hybrid feature space with signal attributes, statistical moments, and randomness measures are extricated from amplitude modulation-frequency modulation components. Single class radial basis function support vector machine is trained with hybrid features. The fault detection accuracy of the proposed method is compared with the standard variants of empirical mode decomposition. The proposed short windowed Fourier synchro squeezing transform-radial basis function kernel support vector machine shows 98.2% accuracy, 98% sensitivity, and 98% specificity.

An Empirical Analysis of Evolved Radial Basis Function Networks and Support Vector Machines with Mixture of Kernels

2015 ◽  
Vol 24 (04) ◽  
pp. 1550013 ◽  
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Pulak Sahoo ◽  
Satchidananda Dehuri ◽  
Sung-Bae Cho
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Radial Basis Function ◽  
Basis Function ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Imbalanced Dataset ◽  
Radial Basis

Classification is one of the most fundamental and formidable tasks in many domains including biomedical. In biomedical domain, the distributions of data in most of the datasets into predefined number of classes is significantly different (i.e., the classes are distributed unevenly). Many mathematical, statistical, and machine learning approaches have been developed for classification of biomedical datasets with a varying degree of success. This paper attempts to analyze the empirical performance of two forefront machine learning algorithms particularly designed for classification problem by adding some novelty to address the problem of imbalanced dataset. The evolved radial basis function network with novel kernel and support vector machine with mixture of kernels are suitably designed for the purpose of classification of imbalanced dataset. The experimental outcome shows that both algorithms are promising compared to simple radial basis function neural networks and support vector machine, respectively. However, on an average, support vector machine with mixture kernels is better than evolved radial basis function neural networks.

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