Query: prediction breast cancer with svm

title: Performance Comparison of Different Machine Learning Algorithms for Risk Prediction and Diagnosis of Breast Cancer
abstract: Breast cancer is the most common life threatening diseases and morbidity is also very high among the women population globally. Manual diagnosis of each image pattern in many cases has not produced any satisfactory outcome as it is time consuming as well as inefficient. In medical field, to classify the data, classification and data mining method play a major role. Diagnosis and analysis process in terms of time and accuracy can be substantially upgraded due to the classifier without human involvement. The main objective of this paper is to evaluate the performance of different machine learning algorithms SVM (Support Vector Machine) K Nearest Neighbors (K NN) Naive Bayes (NB) CART (Classification and Regression Tree) in terms of efficiency and effectiveness. The two parameters of each algorithm have been measured by accuracy, precession, sensitivity and specificity. The SVM achieved highest accuracy (98.14% with lower error rate. The valuation process is carried out on the on the Wisconsin Breast Cancer (original) datasets. The aim of this is to evaluate the suitable classifier by comparing the performance of four algorithms. SVM achieved a significant performance in respect of accuracy (98.14% with lowest error rate.

title: Machine Learning Techniques and Extreme Learning Machine for Early Breast Cancer Prediction
abstract: Breast Cancer is one of the most deadly disease and most of the women are infected by this vital disease in many parts of the world. Medical tests conducted in hospitals for determining the disease are very much expensive as well as timeconsuming. The problem can be resolved by diagnosing the problem in early spam of time and by providing results with more accuracy.In this paper, different machine learning and neural network algorithmhave been studied and compared to predict cancer in early stages so that life can be saved. The dataset available publically for Breast Cancer has been used. Different algorithms compared includeSupport Vector Machine Classification (SVM) K Nearest NeighborClassification (KNN) Decision tree Classification (DT) Random Forest Classification (RF) and Extreme Learning Machine (ELM).All are compared on the basis of Accuracy and processing time are considered as the parameters for comparing analysis. The results reveal that extreme learning machine comes to be the better algorithm.

title: Development of an Ensemble Multi stage Machine for Prediction of Breast Cancer Survivability
abstract: Prediction of cancer survivability using machine learning techniques has become a popular approach in recent years. In this regard, an important issue is that preparation of some features may need conducting difficult and costly experiments while these features have less significant impacts on the final decision and can be ignored from the feature set. Therefore, developing a machine for prediction of survivability, which ignores these features for simple cases and yields an acceptable prediction accuracy, has turned into a challenge for researchers. In this paper, we have developed an ensemble multi stage machine for survivability prediction which ignores difficult features for simple cases. The machine employs three basic learners, namely multilayer perceptron (MLP) support vector machine (SVM) and decision tree (DT) in the first stage to predict survivability using simple features. If the learners agree on the output, the machine makes the final decision in the first stage. Otherwise, for difficult cases where the output of learners is different, the machine makes decision in the second stage using SVM over all features. The developed model was evaluated using the Surveillance, Epidemiology, and End Results (SEER) database. The experimental results revealed that the developed machine obtains considerable accuracy while it ignores difficult features for most of the input samples.

title: Breast Cancer Classification and Prediction using Machine Learning
abstract: Breast cancer is a dominant cancer in women worldwide and is increasing in developing countries where the majority of cases are diagnosed in late stages. The projects that have already been proposed show a comparison of machine learning algorithms with the help of different techniques like the ensemble methods, data mining algorithms or using blood analysis etc. This paper proposed now presents a comparison of six machine learning (ML) algorithms: Naive Bayes (NB) Random Forest (RT) Artificial Neural Networks (ANN) Nearest Neighbour (KNN) Support Vector Machine (SVM) and Decision Tree (DT) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset which is extracted from a digitised image of an MRI. For the implementation of the ML algorithms, the dataset was partitioned into the training phase and the testing phase. The algorithm with the best results will be used as the backend to the website and the model will then classify the cancer as benign or malignant.

title: Radiomics Signatures Based on Multiparametric MRI for the Preoperative Prediction of the HER2 Status of Patients with Breast Cancer.
abstract: The aim of our study was to preoperatively predict the human epidermal growth factor receptor 2 (HER2) status of patients with breast cancer using radiomics signatures based on single parametric and multiparametric magnetic resonance imaging (MRI) METHODS Three hundred six patients with invasive ductal carcinoma of no special type (IDC NST) were retrospectively enrolled. Quantitative imaging features were extracted from fat suppressed T2 weighted and dynamic contrast enhanced T1 weighted (DCE T1) preoperative MRI. Then, three radiomics signatures based on fat suppressed T2 weighted images, DCE T1 images and their combination were developed using a support vector machine (SVM) to predict the HER2 positive vs HER2 negative status of patients with breast cancer. The area under the curve (AUC) accuracy, sensitivity, and specificity were calculated to assess the predictive performances of the signatures.  Twenty eight quantitative radiomics features, namely, 14 texture features, 4 first order features, 9 wavelet features, and 1 shape feature, were used to construct radiomics signatures. The performance of the radiomics signatures for distinguishing HER2 positive from HER2 negative breast cancer based on fat suppressed T2 weighted images, DCE T1 images, and their combination had an AUC of 0.74 (95% confidence interval [CI] 0.700 to 0.770) 0.71 (0.673 to 0.738) and 0.86 (0.832 to 0.882) in the primary cohort and 0.70 (0.666 to 0.744) 0.68 (0.650 to 0.726) and 0.81 (0.776 to 0.837) in the validation cohort, respectively.  Radiomics signatures based on multiparametric MRI represent a potential and efficient alternative tool to evaluate the HER2 status in patients with breast cancer.

title: Analysis of Histopathological Images for Prediction of Breast Cancer Using Traditional Classifiers with Pre Trained CNN
abstract: Breast cancer is one of the most commonly found and dangerous cancer among women which leads to a major research topic in medical science. Most of the times, it is identified by using a biopsy method where tissue is removed and studied under a microscope. If a histopathologist is not well trained then this may lead to the wrong diagnosis. In order to facilitate better diagnosis, automatic analysis of histopathology images can help pathologists to identify malignant tumors and cancer subtypes. Recently, Convolutional Neural Networks (CNNs) have become preferred deep learning approaches for breast cancer classification and detection. In this research, two machine learning approaches i.e. Support Vector Machine (SVM) and Logistic Regression (LR) are used for comparative analysis. This paper mainly focuses on leveraging pre trained (CNN) activation features on traditional classifiers to perform automatic classification of breast cancer images. For this purpose, a two phase model has been proposed for automatic classification on the basis of magnification subsequently classify the samples for benign and malignant. This model is trained separately with respect to various image magnifications (40x, 100x, 200x and 400x) In this study, the dataset is partitioned into the following fashion: 80% for the training phase and 20% for testing phase The performance is analyzed by using Accuracy, Precision, Recall and F1 score in order to find out the best suited model that can be used for automation system. The experimental results demonstrate that ResNet50 network has achieved maximum accuracy for LR in comparison to SVM in magnification factor. In addition, results show that the performance of CNN +LR is slightly better than CNN +SVM for classification of benign and malignant classes. The proposed model helps in extracting more accurate image features and significantly improves the overall accuracy of classification.

title: Cloud Based Breast Cancer Prediction Empowered with Soft Computing Approaches
abstract: The developing countries are still starving for the betterment of health sector. The disease commonly found among the women is breast cancer, and past researches have proven results that if the cancer is detected at a very early stage, the chances to overcome the disease are higher than the disease treated or detected at a later stage. This article proposed cloud based intelligent BCP T1F SVM with 2 variations/models like BCP T1F and BCP SVM. The proposed BCP T1F SVM system has employed two main soft computing algorithms. The proposed BCP T1F SVM expert system specifically defines the stage and the type of cancer a person is suffering from. Expert system will elaborate the grievous stages of the cancer, to which extent a patient has suffered. The proposed BCP SVM gives the higher precision of the proposed breast cancer detection model. In the limelight of breast cancer, the proposed BCP T1F SVM expert system gives out the higher precision rate. The proposed BCP T1F expert system is being employed in the diagnosis of breast cancer at an initial stage. Taking different stages of cancer into account, breast cancer is being dealt by BCP T1F expert system. The calculations and the evaluation done in this research have revealed that BCP SVM is better than BCP T1F. The BCP T1F concludes out the 96.56 percentage accuracy, whereas the BCP SVM gives accuracy of 97.06 percentage. The above unleashed research is wrapped up with the conclusion that BCP SVM is better than the BCP T1F. The opinions have been recommended by the medical expertise of Sheikh Zayed Hospital Lahore, Pakistan, and Cavan General Hospital, Lisdaran, Cavan, Ireland.

title: Machine Learning techniques for Prediction from various Breast Cancer Datasets
abstract: Cancer has become very common disease among Indians. Breast cancer occurs 14% of all cancers in women. One in every 28 women is getting affected by breast cancer  Breast cancer detection and identification are done from gene expression and large datasets. Due to the increase in the generation of large datasets of medical data we can analyze specialized patterns that are hidden inside. To analyze the pattern from datasets machine learning techniques such as SVM, KNN, Decision tree are applied. In this paper, we tried to apply some Deep learning techniques to identify the patterns resides in a reoccurrence of breast cancer datasets, Mammogram datasets and to identify the patterns.

title: Breast Cancer Prediction Using Deep Learning and Machine Learning Techniques
abstract: Breast Cancer is mostly identified among women and is a major reason for increasing the rate of mortality among women. Diagnosis of breast cancer is time consuming and due to the lesser availability of systems it is necessary to develop a system that can automatically diagnose breast cancer in its early stages. Various Machine Learning and Deep Learning Algorithms have been used for the classification of benign and malignant tumours. The Wisconsin Breast Cancer Dataset has been used which contains 569 samples and 30 features. The paper emphasises on various models that is implemented such as Logistic Regression, Support Vector Machine (SVM) and K Nearest Neighbour (KNN) Multi Layer perceptron classifier, Artificial Neural Network(ANN) etc. on the dataset taken from the repository of Kaggle. Each of these algorithms has been measured and compared with respect to accuracy and precision obtained. All the techniques are coded in python and executed in Google Colab, which is a Scientific Python Development Environment. The experiments have shown that SVM and Random Forest Classifier are the best for predictive analysis with an accuracy of 96.5% To increase the accuracy of prediction, deep learning algorithms such as CNN and ANN have been implemented. The maximum accuracy obtained in the case of ANN and CNN are 99.3% and 97.3% respectively. Activation functions such as Relu and sigmoid have been used to predict the outcomes in terms of probabilities.

title: Prediction of survival and metastasis in breast cancer patients using machine learning classifiers
abstract: Breast cancer (BC) is one of the most common malignancies in women. Early diagnosis of BC and metastasis among the patients based on an accurate system can increase survival of the patients to >86% This study aimed to compare the performance of six machine learning techniques two traditional methods for the prediction of BC survival and metastasis.  We used a dataset that include the records of 550 breast cancer patients. Naive Bayes (NB) Random Forest (RF) AdaBoost, Support Vector Machine (SVM) Least square SVM (LSSVM) and Adabag, Logistic Regression (LR) and Linear Discriminant Analysis were used for the prediction of breast cancer survival and metastasis. The performance of the used techniques was evaluated with sensitivity, specificity, likelihood ratio and total accuracy.  Out of 550 patients, 83.4% were alive and 85% did not experience metastasis. In prediction of survival, the average specificity of all techniques was =94% and the SVM and LDA have greater sensitivity (73% in comparison to other techniques. The greater total accuracy (93% belonged to the SVM and LDA. For metastasis prediction, the RF had the highest specificity (98% the NB had highest sensitivity (36% and the LR and LDA had the highest total accuracy (86%  Our finding showed that the SVM outperformed other machine learning methods in prediction of survival of the patients in terms of several criteria. Nevertheless, the LDA technique as a classical method showed similar performance.