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In this paper, we discuss the importance of feature subset selection methods in machine learning techniques. An analysis of microarray expression was used to check whether global biological differences underlie common pathological features for different types of cancer datasets and to identify genes that might anticipate the clinical behavior of this disease. One way of finding relevant gene selection...
This work presents a novel algorithm for recognizing activity states which are of interest for assessing the general well-being of cancer, frail and elderly patients. Using the novel idea of two-level classification, misclassification due to unwanted hand motion noise, which is a common source of error in wrist-worn sensing systems, is mitigated. The algorithm is verified using data from 20 subjects...
Many early stage lung cancer patients have resectable tumors, however, their cardiopulmonary function needs to be properly evaluated before they are deemed operative candidates. Such patients are typically asked to undergo standard pulmonary function tests, including cardiopulmonary exercise tests (CPET) or stair climbs. The standard tests are conducted only at selected healthcare provider locations,...
Melanoma is certainly the deadliest skin cancer. Clinicians try to detect melanoma at early stages in order to increase the successful treatment rate by using dermoscopes. We have designed a digital dermoscope that is both mobile and highly sensitive for automatic classification. We developed an accurate image processing software and a learning program that uses artificial neural network learning...
Ventricular tachycardia, ventricular flutter, and ventricular fibrillation are malignant forms of cardiac arrhythmias, whose occurrence may be a life-threatening event. Several methods exist for detecting these arrhythmias in the electrocardiogram. However, the use of Gaussian process classifiers in this context has not been reported in the current literature. In comparison to the popular support...
Classification of different tumor type are of great significance in problems cancer prediction. Choosing the most relevant qualities from huge microarray expression is very important. It is a most explored subject in bioinformatics because of its hugeness to move forward humans understanding of inherent causing cancer mechanism. In this paper, we aim to classify leukaemia cells. Our approach relies...
Cancer classification is routinely done using gene expression data. With microarray technology, monitoring thousands of genes is an easy task. The reliable and precise classification of different tumour types is very important in cancer classification and drug discovery which is useful in providing better treatment. Microarray gene expression data analysis is extensively used for human cancer diagnosis...
The bioinformatics research of lncRNA has attracted much attention in both academics and industry due to the important roles of gene expression in the genome. LncRNA expression profile has large number of dimensions and small number of samples. In the study of the cancer samples' expression profile, there are a mass of redundant lncRNAs which are unrelated to cancer classification. Selecting key lncRNAs...
Three-dimensional imaging based on radio frequency that exploits the contrast in dielectric properties of tissues may be used as a low-cost, non-invasive and non-ionizing methodology for breast cancer detection. This paper demonstrates the use of various supervised machine learning algorithms in classification of breast tissues into less-dense fatty and dense fibroglandular or malignant classes from...
For classifier ensemble systems, diversity is considered as an important factor for good generalization. In this paper, a classifier ensemble algorithm with relevance-based feature subset selection for classification is proposed. Firstly, a combined maximal class relevance and minimal feature relevance criterion is presented to evaluate candidate features, and to search diverse feature subspaces for...
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...
Algorithms used in data mining techniques are of great importance in the field of health care, especially in the case of getting patterns or models that are undiscovered in databases. In the area of health care, leukemia affects the blood status and can be discovered by using the Blood Cell Counter (CBC). This study aims to predict the leukemia existence by determining the relationships of blood properties...
Big Data generated in exabytes per year has become a watchword of today's research. They are exceptionally afar from the capability of commonly used software tools and also beyond the handling possibility of the single machine architecture. Facing this challenge has activated a requisite to reexamine the data management options. The new avenues of NoSQL Big Data compared to the traditional forms has...
Multitask learning (MTL) is commonly used for jointly optimizing multiple learning tasks. To date, all existing MTL methods have been designed for tasks with feature-vector represented instances, but cannot be applied to structure data, such as graphs. More importantly, when carrying out MTL, existing methods mainly focus on exploring overall commonality or disparity between tasks for learning, but...
Non-small Cell Lung Cancer (NSCLC) is a leading death disease in many countries. Many studies are focusing on exact surgical approaches to treat the disease. The five-year overall survival rate for NSCLC patients is typically predicted by traditional regression models with small samples and data size. In this paper, we introduce machine learning tools with feature selection algorithms and random forests...
Cancer is a deadly disease in which body's cells start dividing enormously and are able to spread in to other tissues. Oral cancer is a kind of cancer, where some abnormal lesions or patches will appear in the oral cavity. Since it is difficult to identify it in the initial stages, it has one of the worst survival rates. The proposed health alert system can help the patients in identifying the disease...
This electronic document is a “live” template and already defines the components of your paper [title, text, heads, etc.] in its style sheet This paper provide a broad review for most important algorithms used in the CAD application for lung tissue diagnostics and highlighted the performance of each distinctive algorithm. Moreover, ROC characteristics have been made for each selected algorithms (support...
In the field of pattern recognition, the study of the gene expression profiles of different tissue samples over different experimental conditions has become feasible with the arrival of microarray-based technology. In cancer research, classification of tissue samples is necessary for cancer diagnosis, which can be done with the help of microarray technology. In this paper, we have presented a Multi...
Big data applications are developed and being explored by the computer science organization, which is classified and accepted by huge data sets collected from sensor networks, online networks, medical agencies, etc. To deal with the difficulty in analysis of data, we conduct research on the novel algorithms for data mining and knowledge discovery through network property. At first, we introduce necessary...
Cancer diagnosis is one of the emerging applications in microarray gene expression data. Feature selection plays a crucial role because of the huge dimensionality and less training and testing samples. Finding a small subset of significant genes from a large set of gene expression data is a challenging task. This paper presents the usage of genetic algorithm as a tool to determine the informative...
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