Classification Of Breast Cancer Data Research Articles

Classification of breast cancer data based on neural network algorithm

In today's world, breast cancer has become the most common malignant tumor, so this paper mainly classifies the features of breast cancer through BP neural network to improve the accuracy of research and judgment. This paper mainly adopts 30 breast cancer feature data of 569 cases, and then obtains the feature data used in this BP neural network through correlation analysis, feature selection and principal component analysis. By training, the BP neural network that is most suitable for this data is constructed from aspects such as the number of hidden layers, loss function, and iteration times, ultimately improving the accuracy to 100%.

SMOTE Variants and Random Forest Method: A Comprehensive Approach to Breast Cancer Classification

This research focused on using machine learning methods for breast cancer diagnosis, considering that breast cancer is the scariest disease for women because it can cause mortality. Not only that, but there is also an increase in breast cancer death rates in women yearly. Early prediction is the right solution to increase life expectancy and reduce mortality rates caused by breast cancer. However, breast cancer data has a problem, namely that the data is imbalanced, which harms the performance of the machine learning method itself. In the data, breast cancer had a Benign class (357 instances) more than the Malignant class (212 instances). Therefore, this study aimed to solve the problem of imbalanced data using the Smote variants and Random Forest approaches in breast cancer classification. The results of this study showed that the Smote approach with Random Forest had the best performance compared to Borderline Smote and Random Forest in the case of breast cancer data classification, where Smote with Random Forest produced an accuracy of 97.3%, sensitivity of 96.9%, and specificity of 97.8%. In comparison, Borderline Smote with Random Forest produced an accuracy of 96.4%, sensitivity of 95.6%, and specificity of 96.9%. The results of this study can contribute to predicting breast cancer using the proposed method, because it has been proven to have high accuracy.

A review of homogenous ensemble methods on the classification of breast cancer data

A review of homogenous ensemble methods on the classification of breast cancer data

Enhancing Breast Cancer Data Classification Performance in Medical Data by Employing GRU-LMA Deep Learning Classifier for Accurate Diagnosis

Enhancing Breast Cancer Data Classification Performance in Medical Data by Employing GRU-LMA Deep Learning Classifier for Accurate Diagnosis

Machine learning models applied in analyzing breast cancer classification accuracy

There have been many attempts made to classify breast cancer data, since this classification is critical in a wide variety of applications related to the detection of anomalies, failures, and risks. In this study machine learning (ML) models are reviewed and compared. This paper presents the classification of breast cancer data using various ML models. The effectiveness of models comparatively evaluated through result using benchmark of accuracy which was not done earlier. The models considered for the study are k-nearest neighbor (kNN), decision tree classifier, support vector machine (SVM), random forest (RF), SVM kernels, logistic regression, Naïve Bayes. These classifiers were tested, analyzed and compared with each other. The classifier, decision tree, gets the highest accuracy i.e. 97.08% among all these models is termed as the best ML algorithm for the breast cancer data set.

Improved classification of breast cancer data using hybrid techniques

Breast cancer is the second leading cancer for women in developed countries including India. Many new cancer detection and treatment approaches were developed. The most effective way to reduce breast cancer deaths is detect it earlier. The frequent occurrence of breast cancer and its serious consequences have attracted world wide attention in recent years. Problems such as low rate of accuracy and poor self-adaptability still exist in traditional diagnosis. In order to solve these problems, an Ada Boost-SVM classification algorithm, Combined with k-means is proposed in this research for the early diagnosis of breast cancer. The effectiveness of the proposed methods are examined by calculating its accuracy, confusion matrix which give important clues to the physicians for early diagnosis of breast cancer

A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification.

The pre-diagnosis of cancer has been approached from various perspectives, so it is imperative to continue improving classification algorithms to achieve early diagnosis of the disease and improve patient survival. In the medical field, there are data that, for various reasons, are lost. There are also datasets that mix numerical and categorical values. Very few algorithms classify datasets with such characteristics. Therefore, this study proposes the modification of an existing algorithm for the classification of cancer. The said algorithm showed excellent results compared with classical classification algorithms. The AISAC-MMD (Mixed and Missing Data) is based on the AISAC and was modified to work with datasets with missing and mixed values. It showed significantly better performance than bio-inspired or classical classification algorithms. Statistical analysis established that the AISAC-MMD significantly outperformed the Nearest Neighbor, C4.5, Naïve Bayes, ALVOT, Naïve Associative Classifier, AIRS1, Immunos1, and CLONALG algorithms in conducting breast cancer classification.

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

Nanocomposite parylene-C memristors with embedded Ag nanoparticles for biomedical data processing

Neuromorphic networks adopt human brain mechanisms and create a promising way to solve various artificial cognitive problems. Memristors, novel circuit design elements, could play the role of hardware synapses in such systems. Memristors based on parylene, safe material for usage within the human body, have already shown their eligibility for neuromorphic applications. The embedding of metal nanoparticles into the parylene layer leads to even better memristive characteristics, but the usage of such memristors in neuromorphic systems is still questionable. In this paper, the characteristics of nanocomposite poly(chloro- p -xylylene) memristors are studied, and the possibility of their training following spike-timing-dependent plasticity rules is demonstrated. Furthermore, a simple spiking neuromorphic network based on such memristors is successfully simulated to solve the task of breast cancer data classification. The results pave the way towards bio-compatible and energy-efficient wearable neuromorphic systems based on parylene memristors. • Parylene-C memristors with embedded Ag nanoparticles are studied for the first time. • These devices demonstrate suitable for neuromorphic applications characteristics. • Studied memristors support bio-inspired STDP learning rules. • Simulated memristive spiking network demonstrates decent classification accuracy.

Using new artificial bee colony as probabilistic neural network for breast cancer data classification

PurposeBreast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications.Design/methodology/approachThe new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast cancer data set.FindingsThe new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming.Originality/valueThe new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification.

Svm Performance Optimization Using PSO for Breast Cancer Classification

Breast cancer is a very serious disease and requires the sufferer to undergo an intensive examination. This study proposes an optimization method for SVM parameters using the particle swarm optimization algorithm to classify breast cancer. The test results show that the classification using SVM with optimization using PSO is able to improve accuracy better than the classification using SVM without optimization, namely the determination of the parameters randomly. Breast cancer data classification accuracy increased to 78.91%. The best parameter values for c, γ, r, and d are 0.252101, 0.053248, 1 and 5, respectively. In this study, the polynomial and RBF kernels in general were able to produce higher accuracy than linear and sigmoid kernels.

An Efficient Hybrid Genetic-Grey Wolf Based Neural Network (G2NN) for Breast Cancer Data Classification

Machine learning is the one of the famous Artificial Intelligence (AI) technique. Data Mining or Machine Learning techniques are most popular in medical diagnosis, classification, forecasting etc. K-Nearest Neighbor, SVM (Support Vector Machine), DT (Decision Tree),RF (Random Forest),NN (Neural Network) are famous classification algorithms. Neural Network is one of the popular techniques, which is used to refine the verdict of breast cancer. A neural network is otherwise known as Artificial Neural Network(ANN), which is mimicking of biological neurons of human brain. Genetic Algorithm (GA) is emerged bio inspired technique. Selection, Crossover, and Mutation are three operations in Genetic Algorithm. The performance of a genetic algorithm depends on the genetic operators, particularly crossover operator. Grey Wolfoptimization algorithm is inspired from hunting of wolf strategy. Alphas, Beta, Gamma are the three levels of processes. In this paper, a novel hybrid Genetic Grey Wolf based Neural Network is introduced and we named it as G2NN. In the field of medical, we need more accuracy when compared to other field, because it relates to human life. Many researchers found new novel ideas for breast cancer data classification using neural network model. Among many diseases, Breast Cancer is one of the unsafe diseases among women in Indiaand in addition to the whole world. The early detection of cancer helps in curing the disease completely. In many research areas Genetic Algorithm and Grey wolf algorithm are used to train neurons in order to yield good accuracy. In this manuscript, a new Genetic Grey Wolf optimizer based Neural Network is introduced and we compare the proposed work with other techniques like SVM(Support Vector Machine),NN (Neural Network), Genetic based Neural Network, Grey wolf based Neural Network and the experimental results of proposed work produced better result. The proposed algorithm produces 98.9 % of accuracy on UCI Wisconsin breast cancer dataset.

Modern Analysis of Financial Statements: Pharmaceutical companies in Iran

Objective: In this paper aims to overcome the problems associated with traditional financial analysis, have studied new methods for financial analysis in the Iran pharmaceutical industry. Methodology: In this regard, single-stage and two-stage DEA has been used. The data has been gathered from financial statements for 2014 and the analysis focused on efficiency. Finally, by pay attention to the second sheet of paper, concluded that DEA single-stage and two-stage complement each other. Results: The results showed that the gabor based unsupervised learning described in the present study was able to produce accurate results in the classification of breast cancer data and the classification rule identified was more acceptable and comprehensible. Conclusion: There is a strong correlation between the two methods, and they don't show the same things so these two methods are complementary. Also not superiority On each other and both should be used with financial ratios. so The results in this research are not in same way with the results saravanan article but But the same with pitchumani research.

Ensemble Feature Selection for Breast Cancer Classification using Microarray Data

For breast cancer data classification, we propose an ensemble filter feature selection approach named ‘EnSNR’. Entropy and SNR evaluation functions are used to find the features (genes) for the EnSNR subset. A Genetic Algorithm (GA) generates the classification ‘model’. The efficiency of the ‘model’ is validated using 10-Fold Cross-Validation re-sampling. The Microarray dataset used in our experiments contains 50,739 genes for each of 32 patients. When our proposed ‘EnSNR’ subset of features is used; as well as giving an enhanced degree of prediction accuracy and reducing the number of irrelevant features (genes), there is also a small saving of computer processing time.

Hybrid BATGSA: a metaheuristic model for classification of breast cancer data

Nature-inspired algorithms have a vast range of applications. One such application is in the field of medical data mining where, major focus is on building models for the classification and prediction of various diseases. Breast cancer has grabbed the interest of numerous researchers because, it is the major killer disease, killing millions of women across the globe. In this paper, we propose a hybrid diagnostic model which is a fusion of Bat algorithm (Bat), Gravitational Search Algorithm (GSA), and Feed-Forward Neural Network (FNN). Here, the potential of the FNN and the advantages of nature inspired algorithms have been exploited to build a hybrid model used for classification of breast cancer data. The proposed model consists of two modules. First, is the training module where the data is properly trained using a feed-forward neural network and the second, is an error minimising module, which is built using Bat and GSA metaheuristic algorithm. The hybrid model minimises the error thus, producing better classification results. The accuracy obtained for Wisconsin Diagnostic Breast Cancer Diagnostic (WBCD) data set is found to be 94.28% and 92.10% for training and testing respectively.

Unbalanced breast cancer data classification using novel fitness functions in genetic programming

Breast Cancer is a common disease and to prevent it, the disease must be identified at earlier stages. Available breast cancer datasets are unbalanced in nature, i.e. there are more instances of benign (non-cancerous) cases then malignant (cancerous) ones. Therefore, it is a challenging task for most machine learning (ML) models to classify between benign and malignant cases properly, even though they have high accuracy. Accuracy is not a good metric to assess the results of ML models on breast cancer dataset because of biased results. To address this issue, we use Genetic Programming (GP) and propose two fitness functions. First one is F2 score which focuses on learning more about the minority class, which contains more relevant information, the second one is a novel fitness function known as Distance score (D score) which learns about both the classes by giving them equal importance and being unbiased. The GP framework in which we implemented D score is named as D-score GP (DGP) and the framework implemented with F2 score is named as F2GP. The proposed F2GP achieved a maximum accuracy of 99.63%, 99.51% and 100% for 60-40, 70-30 partition schemes and 10 fold cross validation scheme respectively and DGP achieves a maximum accuracy of 99.63%, 98.5% and 100% in 60-40, 70-30 partition schemes and 10 fold cross validation scheme respectively. The proposed models also achieves a recall of 100% for all the test cases. This shows that using a new fitness function for unbalanced data classification improves the performance of a classifier.

Proximate Breast Cancer Factors Using Data Mining Classification Techniques

Breast cancer is the most common of all cancers and is the leading cause of cancer deaths in women worldwide. The classification of breast cancer data can be useful to predict the outcome of some diseases or discover the genetic behavior of tumors. Data mining technology helps in classifying cancer patients and this technique helps to identify potential cancer patients by simply analyzing the data. This study examines the determinant factors of breast cancer and measures the breast cancer patient data to build a useful classification model using a data mining approach. In this study of 2397 women, 1022 (42.64%) were diagnosed with breast cancer. Among the four main learning techniques such as: Random Forest, Naive Bayes, Classification and Regression Model (CART), and Boosted Tree model were used for the study. The Random Forest technique had the better accuracy value of 0.9892(95%CI,0.9832 -0.9935) and a sensitivity value of about 92%. This means that the Random Forest learning model is the best model to classify and predict breast cancer based on associated factors.

Private naive bayes classification of personal biomedical data: Application in cancer data analysis

Clinicians would benefit from access to predictive models for diagnosis, such as classification of tumors as malignant or benign, without compromising patients’ privacy. In addition, the medical institutions and companies who own these medical information systems wish to keep their models private when in use by outside parties. Fully homomorphic encryption (FHE) enables computation over encrypted medical data while ensuring data privacy. In this paper we use private-key fully homomorphic encryption to design a cryptographic protocol for private Naive Bayes classification. This protocol allows a data owner to privately classify his or her information without direct access to the learned model. We apply this protocol to the task of privacy-preserving classification of breast cancer data as benign or malignant. Our results show that private-key fully homomorphic encryption is able to provide fast and accurate results for privacy-preserving medical classification.

Emperical Evaluation of Machine Learning algorithms for Breast Cancer Data Classification

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Breast Cancer Diagnosis Using Imbalanced Learning and Ensemble Method

Worldwide, breast cancer is one of the most threatening killers to mid-aged women. The diagnosis of breast cancer aims to classify spotted breast tumor to be Benign or Malignant. With recent developments in data mining technique, new model structures and algorithms are helping medical workers greatly in improving classification accuracy. In this study, a model is proposed combining ensemble method and imbalanced learning technique for the classification of breast cancer data. First, Synthetic Minority Over-Sampling Technique (SMOTE), an imbalanced learning algorithm is applied to selected datasets and second, multiple baseline classifiers are tuned by Bayesian Optimization. Finally, a stacking ensemble method combines the optimized classifiers for final decision. Comparative analysis shows the proposed model can achieve better performance and adaptivity than conventional methods, in terms of classification accuracy, specificity and AuROC on two mostly-used breast cancer datasets, validating the clinical value of this model.