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Lung cancer is the number one cause of cancer deaths in both men and women in the worldwide. The two types of lung cancer, which grow and spread differently, are the small cell lung cancers (SCLC) and non-small cell lung cancers (NSCLC). Treatment of lung cancer can involve a combination of surgery, chemotherapy, and radiation therapy as well as newer experimental methods. The general prognosis of...
Complex cancer care requires careful coordination, but resource limitations result in lack of effective coordinated follow-up services. Recent advances in smartphones offer great opportunities for better segmentation of patient populations for cost-effective, targeted care coordination and monitoring or surveillance of cancer patients. This paper presents a framework of a smartphone app that provides...
One of the applications of data mining isdisease diagnosis for this purpose one needs medicaldataset to identify hidden patterns and finally extractsuseful knowledge from medical database. Recently, researchers have used different classification andclustering algorithms for diagnosing diseases. Thispaper provides survey on two different complexdiseases which includes the heart disease and Cancerdisease,...
Identifying performance of classifier is a challenging task. SVM plays an important role in classification. Here different kernel parameters are used as a tuning parameter to improve the classification accuracy. There are mainly four different types of kernels (Linear, Polynomial, RBF, and Sigmoid) that are popular in SVM classifier. The paper presents SVM classification results with above mentioned...
Feature Selection(FS) is an important step to enhance the classification accuracy. Using the lazy learning classification algorithm, the feature selection methods calculate relevancy to reduce the storage. Dimensionality reduction technique on scientific data is a popular area to understand the underlying scientific knowledge in a data set, resulted from scientific experiments. This paper presents...
Data mining tools have been around for several decades, but the term “big data” has only recently captured widespread attention. Numerous success stories have been promulgated as organizations have sifted through massive volumes of data to find interesting patterns that are, in turn, transformed into actionable information. Yet a key drawback to the big data paradigm is that it relies on observational...
Although the genomics data are accumulated in an exponential growth, the molecular complexity of cancer is still hard to understand. The most remarkable characteristics of the genomic data are severely high-dimensional features with a small number of samples, such as gene expression data. The traditional data mining method has a limited ability to process these asymmetry datasets. In order to select...
Exceptional Model Mining strives to find coherent subgroups of the dataset where multiple target attributes interact in an unusual way. One instance of such an investigated form of interaction is Pearson's correlation coefficient between two targets. EMM then finds subgroups with an exceptionally linear relation between the targets. In this paper, we enrich the EMM toolbox by developing the more general...
Big data is one of the hottest topics in information science. It has become the key for biological and medical discovery and research, making developing new methods for data management, analysis and accessibility a great challenge in the field. This study proposes an integrated gene analysis approach, in terms of classification and prediction methods for understanding, analyzing and interpretation...
Feature classification plays an important role in computer-aided diagnosis (CADx) of suspicious lesions. While many texture features have been extracted and applied for various clinical purposes, Haralick's feature extraction method is of great interest, because it gives a series of texture measures on the image intensity correlations among the image pixels across an image slice. Based on the Haralick's...
Experiments and generally data in the real world are unbalanced, that is the classification categories are not approximately equally presented because of subject mortality, non-response, etc. The term "Unbalanced" in this context is relative to the distribution of records among the target classes. The various limitations of working with an unbalanced data are discrepancies in calculating...
In this paper, we discuss the evaluation of the probabilistic extraction as introduced in [1], by considering three different datasets introduced in [1] -- [3]. the results show the potential of the approach, as well as its reliability and efficiency when analyzing datasets with different properties and structures. This is part of ongoing research aiming to provide a tool to extract, assess and visualize...
Associative Classification is a recent and rewarding approach which combines associative rule mining and classification. This technique has attracted many researchers as it derives accurate classifier with effective rules. Associative classifiers are useful for application where maximum predictive accuracy is desired. Healthcare industry collects large amounts of data which are not mined to discover...
Data mining plays a key role in Health Monitoring Systems (HMS). Architecture for analyzing data in HMS is described in this paper using data mining techniques. A method is also provided to identify the impact of surrounding geographical environment on health of people. Data mining driven methods are proposed to analyze the health situation of people, to assess threats to their health and to alert...
Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co-occurrence-based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction...
All over the world, a large number of people are suffering from brain related diseases. Diagnosing of these diseases is the requirement of the day. Dementia is one such brain related disease. This causes loss of cognitive functions such as reasoning, memory and other mental abilities which may be due to trauma or normal ageing. Alzheimer's disease is one of the types of the dementia which accounts...
In data mining, decision tree algorithms are very popular methodologies since the algorithms have a simple inference mechanism and provide a comprehensible way to represent the model in the form of a decision tree. Over the past years, fuzzy decision tree algorithms have been proposed in order to provide a way to handle uncertainty in the data collected. Fuzzy decision tree algorithms have shown to...
As the first cancer-causing human virus identified, Epstein-Barr virus (EBV) has been implicated in the development of a wide range of B cell lymphoproliferative disorders, a subset of T/NK cell lymphomas, and post-transplant lymphoproliferative disorders. We made use of the immunological data on EBV available through publications, technical reports, and databases and constructed Epstein-Barr virus...
The development of new technologies, information systems, decision support systems and clinical parameters prediction algorithms using machine learning and data mining, opens a new outlook in many areas of health. In this context, the concept of Quality of Life (QOL) has relevance in health and the possibility of integrate this measure in developing systems Decision Support Clinic (SADC). Through...
To employ and develop the performance of the dimensionality reduction for microarray data there is need of good dimension reduction technique. High-dimensional data bring great challenges in terms of computational complexity and classification performance. Therefore, it is necessary to effectively compress in a low-dimensional feature space from high dimensional feature space to design a learner with...
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