scholarly journals Support Vector Machine Classifier for Prediction of Breast Malignancy using Wisconsin Breast Cancer Dataset

2021 ◽  
Vol 7 (3) ◽  
pp. 57-60
Author(s):  
Reddy Anuradha
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Support Vector Machine Classifier ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Breast Malignancy

scholarly journals RFSVM: A Novel Classification Technique for Breast Cancer Diagnosis

2019 ◽  
Vol 8 (12) ◽  
pp. 3295-3305
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Random Forest ◽  
Early Stage ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Limited Data ◽  
Cancer Dataset ◽  
Cancerous Cell

Cancer is a disease, which develops, in human body due to gene mutation. Due to various factor cells turn into cancerous cell and grow rapidly while damaging normal cells. Many women get affected by breast cancer, which might even cause death if not treated at early stage. Early detection of breast cancer is highly important to increase the survival rate. Machine learning methods and technologies are making it possible to classify and detect the class in an accurate manner. Among other classifiers, random forest and support vector machine are two classifiers that have a good classification power. In this, research a combination of these two classifier i.e. Random Forest and Support Vector Machine (RFSVM) is proposed for early diagnosis of breast cancer cell using Wisconsin Breast Cancer Dataset (WBCD). Using different train-test data ratio experiments are performed and an average of more than 98percentage accuracy is achieved using this hybrid classifier. This paper overcomes the over-fitting problem of random forest and the need of tuning the parameters of Support Vector Machine. Even with limited data available, the classifier tunes its parameters so well to give a highly accurate result.

scholarly journals Classifications of Breast Cancer Diagnosis using Machine Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Breast Cancer Diagnosis ◽  
Performance Comparison ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
K Nearest Neighbors ◽  
Cancer Dataset ◽  
Machine Learning Classification

Breast Cancer (BC) is amongst the most common and leading causes of deaths in women throughout the world. Recently, classification and data analysis tools are being widely used in the medical field for diagnosis, prognosis and decision making to help lower down the risks of people dying or suffering from diseases. Advanced machine learning methods have proven to give hope for patients as this has helped the doctors in early detection of diseases like Breast Cancer that can be fatal, in support with providing accurate outcomes. However, the results highly depend on the techniques used for feature selection and classification which will produce a strong machine learning model. In this paper, a performance comparison is conducted using four classifiers which are Multilayer Perceptron (MLP), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest on the Wisconsin Breast Cancer dataset to spot the most effective predictors. The main goal is to apply best machine learning classification methods to predict the Breast Cancer as benign or malignant using terms such as accuracy, f-measure, precision and recall. Experimental results show that Random forest is proven to achieve the highest accuracy of 99.26% on this dataset and features, while SVM and KNN show 97.78% and 97.04% accuracy respectively. MLP shows the least accuracy of 94.07%. All the experiments are conducted using RStudio as the data mining tool platform.

scholarly journals Identification of Bio-Markers for Breast Cancer Detection through Data Mining Methods

2019 ◽  
Vol 8 (2S3) ◽  
pp. 763-769
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Feature Selection ◽  
Discriminant Analysis ◽  
Classification Tree ◽  
Partial Least Square ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Breast Cancer Dataset

Worldwide, breast cancer is the leading type of cancer in women accounting for 25% of all cases. Survival rates in the developed countries are comparatively higher with that of developing countries. This had led to the importance of computer aided diagnostic methods for early detection of breast cancer disease. This eventually reduces the death rate. This paper intents the scope of the biomarker that can be used to predict the breast cancer from the anthropometric data. This experimental study aims at computing and comparing various classification models (Binary Logistic Regression, Ball Vector Machine (BVM), C4.5, Partial Least Square (PLS) for Classification, Classification Tree, Cost sensitive Classification Tree, Cost sensitive Decision Tree, Support Vector Machine for Classification, Core Vector Machine, ID3, K-Nearest Neighbor, Linear Discriminant Analysis (LDA), Log-Reg TRIRLS, Multi Layer Perceptron (MLP), Multinomial Logistic Regression (MLR), Naïve Bayes (NB), PLS for Discriminant Analysis, PLS for LDA, Random Tree (RT), Support Vector Machine SVM) for the UCI Coimbra breast cancer dataset. The feature selection algorithms (Backward Logit, Fisher Filtering, Forward Logit, ReleifF, Step disc) are worked out to find out the minimum attributes that can achieve a better accuracy. To ascertain the accuracy results, the Jack-knife cross validation method for the algorithms is conducted and validated. The Core vector machine classification algorithm outperforms the other nineteen algorithms with an accuracy of 82.76%, sensitivity of 76.92% and specificity of 87.50% for the selected three attributes, Age, Glucose and Resistin using ReleifF feature selection algorithm.

HIOC: a hybrid imputation method to predict missing values in medical datasets

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooja Rani ◽  
Rajneesh Kumar ◽  
Anurag Jain
Keyword(s):  
Breast Cancer ◽  
Heart Disease ◽  
Missing Values ◽  
Imputation Method ◽  
Support Vector ◽  
Correct Prediction ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Content Type ◽  
Imputation Methods

PurposeDecision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases. However, the performance of these systems is adversely affected by the missing values in medical datasets. Imputation methods are used to predict these missing values. In this paper, a new imputation method called hybrid imputation optimized by the classifier (HIOC) is proposed to predict missing values efficiently.Design/methodology/approachThe proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations (MICE), K nearest neighbor (KNN), mean and mode imputation methods in an optimum way. Performance of HIOC has been compared to MICE, KNN, and mean and mode methods. Four classifiers support vector machine (SVM), naive Bayes (NB), random forest (RF) and decision tree (DT) have been used to evaluate the performance of imputation methods.FindingsThe results show that HIOC performed efficiently even with a high rate of missing values. It had reduced root mean square error (RMSE) up to 17.32% in the heart disease dataset and 34.73% in the breast cancer dataset. Correct prediction of missing values improved the accuracy of the classifiers in predicting diseases. It increased classification accuracy up to 18.61% in the heart disease dataset and 6.20% in the breast cancer dataset.Originality/valueThe proposed HIOC is a new hybrid imputation method that can efficiently predict missing values in any medical dataset.

scholarly journals Chaotic Duck Traveler Optimization (cDTO) Algorithm for Feature Selection in Breast Cancer Dataset Problem

2021 ◽  
Vol 12 (4) ◽  
pp. 250-262
Author(s):  
Krishnaveni Arumugam, Et. al.
Keyword(s):  
Breast Cancer ◽  
The United States ◽  
Image Features ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Linear Discriminant ◽  
Algorithm Efficiency ◽  
Breast Malignancy ◽  
Analysis Society ◽  
Mammogram Image

Objective: 1 of every 3 individuals will be determined to have malignancy in the course of their life. Currently, there are more than 3.8 million ladies who have been determined to have breast malignancy in the United States. 2021 is practically around the bend, yet there's still an ideal opportunity to help ladies confronting breast malignancy in 2020. In this paper, chaotic based duck travel optimization (cDTO) meta-heuristic algorithm is introduced to classifying the input images from Mammogram Image Analysis Society (MIAS) database. Methods: Linear Discriminant Analysis is used to extract the mammogram image features. (cDTO-LDA) is an intrinsic algorithm to remove irrelevant features and select the optimal features by using wavelet families Haar (harr), db4 (daubechies), bior4.4 (Biorthogonal), Symlets (SYM8), “Discrete” FIR approximation of Meyer wavelet (dmey) features. Results: These selected features are evaluated by the quality measures such as accuracy, sensitivity, specificity, error rate that are clearly shows the high exactness of cDTO classifier is 98.5%. CSA-LDA classifier has the minimum exactness. Conclusion: Algorithm efficiency is proved by the promising results achieved by the proposed algorithm for selecting the best feature of breast cancer classification.

scholarly journals Improved Cost-Sensitive Support Vector Machine Classifier for Breast Cancer Diagnosis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Na Liu ◽  
Jiang Shen ◽  
Man Xu ◽  
Dan Gan ◽  
Er-Shi Qi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Diagnosis ◽  
Classification Accuracy ◽  
Support Vector Machine Classifier ◽  
Treatment Plan ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Classification Models ◽  
Misclassification Costs

As one of the most prevalent cancers among women worldwide, breast cancer has attracted the most attention by researchers. It has been verified that an accurate and early detection of breast cancer can increase the chances for the patients to take the right treatment plan and survive for a long time. Nowadays, numerous classification methods have been utilized for breast cancer diagnosis. However, most of these classification models have concentrated on maximum the classification accuracy, failed to take into account the unequal misclassification costs for the breast cancer diagnosis. To the best of our knowledge, misclassifying the cancerous patient as non-cancerous has much higher cost compared to misclassifying the non-cancerous as cancerous. Consequently, in order to tackle this deficiency and further improve the classification accuracy of the breast cancer diagnosis, we propose an improved cost-sensitive support vector machine classifier (ICS-SVM) for the diagnosis of breast cancer. In the proposed approach, we take full account of unequal misclassification costs of breast cancer intelligent diagnosis and provide more reasonable results over previous works and conventional classification models. To evaluate the performance of the proposed approach, Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) breast cancer datasets obtained from the University of California at Irvine (UCI) machine learning repository have been studied. The experimental results demonstrate that the proposed hybrid algorithm outperforms all the existing methods. Promisingly, the proposed method can be regarded as a useful clinical tool for breast cancer diagnosis and could also be applied to other illness diagnosis.

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 Linear discriminant analysis and support vector machines for classifying breast cancer

2021 ◽  
Vol 10 (1) ◽  
pp. 253
Author(s):  
Zuherman Rustam ◽  
Yasirly Amalia ◽  
Sri Hartini ◽  
Glori Stephani Saragih
Keyword(s):  
Breast Cancer ◽  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Linear Discriminant ◽  
Specialist Doctor ◽  
Vector Machines

<span id="docs-internal-guid-4db59d91-7fff-c659-478a-6dd7456f380f"><span>Breast cancer is an abnormal cell growth in the breast that keeps changed uncontrolled and it forms a tumor. The tumor can be benign or malignant. Benign could not be dangerous to health and cancerous, but malignant could be has a probability dangerous to health and be cancerous. A specialist doctor will diagnose the patient and give treatment based on the diagnosis which is benign or malignant. Machine learning offer times efficiency to determine a cancer cell. The machine will learn the pattern based on the information from the dataset. Support vector machines and linear discriminant analysis are common methods that can be used in the classification of cancer. In this study, both of linear discriminant analysis and support vector machines are compared by looking from accuracy, sensitivity, specificity, and F1-score. We will know which methods are better in classifying breast cancer dataset. The result shows that the support vector machine has better performance than the linear discriminant analysis. It can be seen from the accuracy is 98.77%.</span></span>

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