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In this paper, several ensemble cancer survivability predictive models are presented and tested based on three variants of AdaBoost algorithm. In the models we used Random Forest, Radial Basis Function Network and Neural Network algorithms as base learners while AdaBoostM1, Real AdaBoost and MultiBoostAB were used as ensemble techniques and ten other classifiers as standalone models. There has been...
In this paper, a novel approach for classification rule mining is presented. The remarkable relationship between the rule extraction procedure and the concept of multiobjective optimization is emphasized. The range values of features composing the rules are handled as decision variables in the modelled multiobjective optimization problem. The proposed method is applied to three well-known datasets...
Breast cancer became one of the deadliest cancer in women. It occurs when the growth of the cells in breast tissue become out of control. Cells are the building blocks for the organs and tissues in the body. When the growth of new cells are uncontrolled then they build-up mass of tissue called tumor. The tumors are categorized in to benign and malignant tumors. Early diagnosis needs an accurate diagnosis...
The second largest cause of death in Palestine is Cancer at a rate 12.4% of all deaths. Predicting the survivability of a disease is one of the most interesting purposes of developing a medical data mining applications. This paper applies two classification models (Rule Induction and Random Forest) on the Gaza Strip 2011 cancer patient's dataset, to predict the survivability of cancer patients. The...
Now a days people are enjoying the world of data because size and amount of the data has tremendously increased which acts like an invitation to Big data. But some of the classifier techniques like Support Vector Machine (SVM) is not able to handle the huge amount of data due to it's excessive memory requirement and unreasonable complexity in algorithm tough it is one of the most popularly used classifier...
Predicting the survival status of patients who will undergo breast cancer surgery is highly important, where it indicates whether conducting a surgery is the best solution for the presented medical case or not. Since this is a case of life or death, the need to explore better prediction techniques to ensure accurate survival status prediction cannot be overemphasized. In this paper we evaluate the...
Breast Cancer is highly predominant in women in today's world. It can start in the breast and can spread to other areas of the body in the course of time. Breast cancer is the second largest disease leading to the death of women. The disease is curable if detected early enough. A lot of research is being done to detect the cancer at the earliest. Early detection at the microcalcification stage can...
Now a days people are enjoying the world of data because size and amount of the data has tremendously increased which acts like an invitation to Big data. But some of the classifier techniques like Support Vector Machine (SVM) is not able to handle the huge amount of data due to it's excessive memory requirement and unreasonable complexity in algorithm tough it is one of the most popularly used classifier...
Breast cancer is invasive cancer among world's women above 35 years of age. The most common symptoms of breast cancer are lumps, change in shape/skin colour and liquid oozing out from nipple. Breast cancer mostly starts from breast tissues that are either in lobules or in milk ducts. Ductal carcinoma is the common type of breast cancer starts from milk ducts and spread across the. Women between the...
The experimental results show that the classification result with the decision trees algorithm come up over the other classifier. The decision tree algorithm creates a predictive model that predicts the state of the affected tissue by learning simple decision rules inferred while learning.
Breast cancer is a major threat for middle aged women throughout the world and currently this is the second most threatening cause of cancer death in women. But early detection and prevention can significantly reduce the chances of death. An important fact regarding breast cancer prognosis is to optimize the probability of cancer recurrence. This paper aims at finding breast cancer recurrence probability...
Recent studies in breast cancer domains have identified seven distinct clinical phenotypes (groups) using immunohistochemical analysis and a variety of unsupervised learning techniques. Consensus among the clustering algorithms has been used to categorise patients into these specific groups, but often at the expenses of not classifying all patients. It is known that fuzzy methodologies can provide...
An optimal classification model for classifying on a given problem should comprise of a classifier, a proper feature subset and a parameter set such that the classifier can attain high prediction performance as possible. Many recent feature selection methods are either too exhaustive or too greedy. Besides, many classification approaches conduct parameter search after feature selection stage, resulting...
Early diagnosis of Breast Cancer is significantly important to treat the disease easily therefore it is necessary to develop techniques that can help physicians to get accurate diagnosis. This study suggests a hybrid classification algorithm which is based upon Genetic Algorithm (GA) and k Nearest neighbor algorithm (kNN). GA algorithm has been used for its primary purpose as an optimization technique...
Breast cancer is one of the major public health problem for women throughout the world. It has two states, known as benign and malignant. Benign state is slow growing, rarely spread to other parts of body and have well-defined borders. On the other hand, Malignant state has tendency to grow faster and it is life threatening. So, classification of this two state is crucial for proper diagnosis of a...
Breast cancer is one of the most common forms of cancer in women worldwide. Most cases of breast cancer can be prevented through screening programs aimed at detecting abnormal tissue. So, early detection and diagnosis is the best way to cure breast cancer to decrease the mortality rate. Computer Aided Diagnosis (CAD) system provides an alternative tool to the radiologist for the screening and diagnosis...
The computer-aided histopathological image diagnosis has attracted considerable attention. Principal component analysis network (PCANet) is a novel deep learning algorithm with a simple network architecture and parameters. In this work, we propose a random binary hashing (RBH) based PCANet (RBH-PCANet), which can generate multiple randomly encoded binary codes to provide more information. Moreover,...
Breast cancer is the most common malignant tumor in women worldwide. In recent years, there has been an increasing use of immunohistochemistry (the process of detecting the expression of certain proteins in cytological images) to obtain useful information for diagnosis. This paper presents an efficient algorithm that automatically detects breast cancer cell nuclei and divides them into two groups:...
Machine learning algorithms are computer programs that try to predict cancer type based on the past data. The eventual goal of Machine learning algorithms in cancer diagnosis is to have a trained machine learning algorithm that gives the gene expression levels from cancer patient, can accurately predict what type and severity of cancer they have, aiding the doctor in treating it. The existing technology...
Classification of cancer patients into treatment groups is essential for appropriate diagnosis to increase survival. Previously, a series of papers, largely published in the breast cancer domain have leveraged Computational Intelligence (CI) developments and tools, resulting in ground breaking advances such as the classification of cancer into newly identified classes — leading to improved treatment...
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