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 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.

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.

scholarly journals Breast Cancer Diagnosis (BCD) Model Using Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 4456-4463
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Principal Component ◽  
Feature Space ◽  
Breast Cancer Diagnosis ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Calibration Plot ◽  
K Nearest Neighbors ◽  
Diagnose Breast Cancer

In the recent years, breast cancer research has made a significant growth however there is still a scope of advancement. Breast cancer increases the statistics of mortality among women. In concern to this issue, treatment of cancer should be started at the earlier stage, to increase the chances of survival of the patient. Thus, there is a need to diagnose breast cancer at the early stage using the features from the mammograms. This paper proposes an efficient BCD model to detect breast cancer by using Support Vector Machine (SVM) with 10-fold cross validation. The complexity of the problem increases if there are many input features for the diagnosis of cancer. Thus, Principal Component Analysis (PCA) is used to reduce the feature space from a higher dimension to a lower dimension. Experiment result shows that the PCA increases the accuracy of the model. The proposed BCD model is compared with other supervised learning algorithms like Decision trees (DT), Random Forest, k- Nearest Neighbors(k-NN), Stochastic Gradient Descent (SGD), AdaBoost, Neural Network (NN), and Naïve Bayes. Evaluation parameters like F1 measure, ROC curve, Accuracy, Lift curve and Calibration Plot proves that proposed BCD model outperforms and gives the highest accuracy among other compared algorithms

scholarly journals A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

2021 ◽  
pp. 45-59
Author(s):  
Shler Farhad Khorshid ◽  
Adnan Mohsin Abdulazeez ◽  
Amira Bibo Sallow
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Mining ◽  
Nearest Neighbors ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Wide Range

Breast cancer is one of the most common diseases among women, accounting for many deaths each year. Even though cancer can be treated and cured in its early stages, many patients are diagnosed at a late stage. Data mining is the method of finding or extracting information from massive databases or datasets, and it is a field of computer science with a lot of potentials. It covers a wide range of areas, one of which is classification. Classification may also be accomplished using a variety of methods or algorithms. With the aid of MATLAB, five classification algorithms were compared. This paper presents a performance comparison among the classifiers: Support Vector Machine (SVM), Logistics Regression (LR), K-Nearest Neighbors (K-NN), Weighted K-Nearest Neighbors (Weighted K-NN), and Gaussian Naïve Bayes (Gaussian NB). The data set was taken from UCI Machine learning Repository. The main objective of this study is to classify breast cancer women using the application of machine learning algorithms based on their accuracy. The results have revealed that Weighted K-NN (96.7%) has the highest accuracy among all the classifiers.

scholarly journals Breast Cancer Prediction Using Machine Learning Algorithm with Big Data Concept

2020 ◽  
pp. 123-127
Author(s):  
R. Nirmalan ◽  
M. Javith Hussain Khan ◽  
V. Sounder ◽  
A. Manikkaraja
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Learning Algorithm ◽  
Support Vector ◽  
Data Set ◽  
Cancer Prediction ◽  
Modern Computer ◽  
Huge Data

The evolution in modern computer technology produce an huge amount of data by the way of using updated technology world with the lot and lot of inventions. The algorithms which we used in machine-learning traditionally might not support the concept of big data. Here we have discussed and implemented the solution for the problem, while predicting breast cancer using big data. DNA methylation (DM) as well gene expression (GE) are the two types of data used for the prediction of breast cancer. The main objective is to classify individual data set in the separate manner. To achieve this main objective, we have used a platform Apache Spark. Here,we have applied three types of algorithms used for classification, they are decision tree, random forest algorithm, support vector machine algorithm which will be mentioned as SVM .These three types of algorithm used for producing models used for breast cancer prediction. Analyze have done for finding which algorithm will produce the better result with good accuracy and less error rate. Additionally, the platforms like Weka and Spark are compared, to find which will have the better performance while dealing with the huge data. The obtained outcome have proved that the Support Vector Machine classifier which is scalable might given the better performance than all other classifiers and it have achieved the lowest error range with the highest accuracy using GE data set

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.

SVM Kernel and Genetic Feature Selection Based Automated Diagnosis of Breast Cancer

2020 ◽  
Vol 13 ◽  
Author(s):  
Indu Singh ◽  
Shashank Garg ◽  
Shivam Arora ◽  
Nikhil Arora ◽  
Kripali Agrawal
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Early Stage ◽  
Feature Space ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Human Beings ◽  
Novel Approach

Background: Breast cancer is the development of a malignant tumor in the breast of human beings (especially females). If not detected at the initial stages, it can substantially lead to an inoperable construct. It is a reason for majority of cancer-related deaths throughout the world. Objectives: The main aim of our study is to diagnose the breast cancer at early stage so that required treatment can be provided for survival. The tumor is classified as malignant or benign accurately at early stage using a novel approach that includes an ensemble of Genetic Algorithm for feature selection and kernel selection for SVM-Classifier. Methods: The proposed GA-SVM (Genetic Algorithm – Support Vector Machine) algorithm in this paper optimally selects the most appropriate features for training with the SVM classifier. Genetic Programming is used to select the features and the kernel for the SVM classifier. Genetic Algorithm operates by exploring the optimal layout of features for breast cancer, thus, subjugating the problems faced in exponentially immense feature space. Results: The proposed approach accounts for a mean accuracy of 98.82% by using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset available on UCI with the training and testing ratio being 50:50 respectively. Conclusion: The results prove that our proposed model outperforms the previously designed models for breast cancer diagnosis. The outcome assures that the GA-SVM model may be used as an effective tool in assisting the doctors for treating the patients. Alternatively, it may be utilized as an alternate opinion in their eventual diagnosis.

scholarly journals Machine Learning Based Suspicion of Customer Detention in Banking with Diverse Solver Neighbors and Kernels

2019 ◽  
Vol 8 (4) ◽  
pp. 3244-3249
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Confusion Matrix ◽  
Principal Component ◽  
Support Vector ◽  
Business Sector ◽  
K Nearest Neighbors ◽  
Data Set ◽  
Customer Churn ◽  
Rbf Kernel

In the current moving technological business sector, the amount spent for attaching the new customer is highly expensive and time consuming process than adopting some methods to hold and retain the existing customers. So the business sector is in need to make a research on with holding the existing customers by using the current technology. The methods to make the retention of the existing customers with high reliablility are a challenging task. With this view, we focus on predicting the customer churn for the banking application. This paper uses the customer churn bank modeling data set extracted from UCI Machine Learning Repository. The anaconda Navigator IDE along with Spyder is used for implementing the Python code. Our contribution is folded is folded in three ways. First, the data preprocessing is done and the relationship between the attributes are identified. Second, the data set is reduced with the principal component analysis to form the 2 component feature reduced dataset. Third, the raw dataset and 2 component PCA reduced dataset is fitted to various solvers of logistic regression classifiers and the performance is analyzed with the confusion matrix. Fourth, the raw dataset and 2 component PCA reduced dataset is fitted to various neighboring algorithms of K-Nearest Neighbors classifiers and the performance is analyzed with the confusion matrix. Fifth, the raw dataset and 2 component PCA reduced dataset is fitted to various kernels of Support Vector Machine classifiers and the performance is analyzed with the confusion matrix. The implementation is carried out with python code using Anaconda Navigator. Experimental results shows that, the rbf kernel of Support vector machine classifier is effective with the accuracy of 85.8% before applying PCA and accuracy of 80.9% after applying PCA compared to other classifiers.

scholarly journals A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 410
Author(s):  
Domenico Pomarico ◽  
Annarita Fanizzi ◽  
Nicola Amoroso ◽  
Roberto Bellotti ◽  
Albino Biafora ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Feature Selection ◽  
Cancer Patients ◽  
Early Stage ◽  
Support Vector ◽  
Training Procedure ◽  
Sampled Data ◽  
Breast Cancer Patients ◽  
Data Set

Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation.

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