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In this study, in order to take advantage of complementary information from different types of data for better disease status diagnosis, we combined gene expression with DNA methylation data and generated a fused network, based on which the stages of Kidney Renal Cell Carcinoma (KIRC) can be better identified. It is well recognized that a network is important for investigating the connectivity of...
This paper presents a new approach to the production of feature maps for the improvement of classification in machine learning. The idea is based on a calculus of differentiation and integration of feature vectors, which can be viewed as functions on a metric space or network. Based on this we propose a novel network-based binary machine learning classifier. We illustrate our method using molecular...
Magnetic Resonance Imaging (MRI) has become an important tool for doctors to diagnose liver cancer for decays. The survival rate of liver cancer patients can be significantly improved by an early diagnosis. In this paper, we present a computer aided kernel based support vector machine (SVM) algorithm for diagnosing liver cancer in early stage by applying our proposed method to the patients' magnetic...
We apply two techniques of classification, Least Squares Support Vector Machines (LS-SVM) and Sequential Minimum Optimization SVM (SMO-SVM) to some diseases: cancer, hepatitis, heart, thyroid, and diabetes, described in Benchmark data sets. To compare between these techniques, some kernel functions are used which are polynomial, linear, sigmoidal, Gaussian and beta. Therefore the classifier β — LS...
Medicine is one of the major fields where the application of artificial intelligence primarily deals with construction of programs that perform diagnosis and make therapy recommendations. In digital mammography, data mining techniques are used to detect and characterize abnormalities in images and clinical reports. In the existing approaches, the mammogram image classification is done in either clinical...
Accurate and less invasive personalized predictive medicine relieves many breast cancer patients from agonizingly complex surgical treatments, their colossal costs and primarily letting the patient to forgo the morbidity of a treatment that proffers no benefit. Cancer prognosis estimates recurrence of disease and predict survival of patient; hence resulting in improved patient management. Support...
This paper presents a new method for performing supervised learning (classification) and demonstrates the technique by applying it to the detection of breast cancer from the dynamic information obtained in magnetic resonance imaging examinations. The proposed method is a vector machine similar to the established support vector machine (SVM) method, however, our method involves a reformulation of the...
We present a technique for automatic diagnosis of malignant melanoma based exclusively on local pattern analysis. The technique relies on local binary patterns in small sections in the image, and automatically selects the relevant texture features from those that discriminate best between benign and malignant skin lesions. The classification is performed using support vector machines, and the feature...
Mass in mammogram can be an indicator of breast cancer. In this work we propose a new approach using twin support vector machine (TWSVM) for automated detection of mass in digital mammograms. This algorithm finds two hyperplanes to classify data points into different classes according to the relevance between a given point and either plane. It works much faster than original SVM classifier. The proposed...
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