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This paper aims to conduct supervised learning of the cigarette-smoking signatures from the placental gene expression data sets under the neural network framework and build classifiers to identify the cigarette-smoking moms during pregnancy. First, a unified model for gene selection is proposed to single out a set of informative gene sets (up-or down-regulated genes). The selected signature gene sets...
Cooperations between engineers and physicians are crucial for studying and solving complex medical-biological problems. The study of obesity - unanimously regarded as a multi-factorial disease - is a typical example where specialists from various areas of medical research may be supported by engineers expert in system theory and software development. The effectiveness and the risk-benefit profile...
This research paper evaluates the ability of Artificial Neural Networks (ANN) to predict the performance of the applicants students to Medical Sciences, in order to predict their failure/ risk in their premedical year. Educational institutions in general, consider a variety of factors when making admission decisions. Traditionally, academic researchers have developed several statistical models to...
Ubiquitous health (U-Health) system witch focuses on automated applications that can provide healthcare to human anywhere and anytime using wired and wireless mobile technologies is becoming increasingly important. This system consists of a network system to collect data and a sensor module which measures pulse, blood pressure, diabetes, blood sugar, body fat diet with management and measurement of...
Background: The diagnosis of cancer in most cases depends on a complex combination of clinical and histopathological data. Because of this complexity, there exists a significant amount of interest among clinical professionals and researchers regarding the efficient and accurate prediction of breast cancers. Results: In this paper, we develop a breast cancer prognosis predict system that can assist...
This paper presents a classification system for cardiac arrhythmias using artificial neural network (ANN) with back propagation algorithm. Classifiers based on multi layer perceptron (MLP) and discriminant analysis study using XLSTAT statistical classifier software are thoroughly examined on the UCI machine learning data base for cardiac arrhythmias. For this multi class classification we used one...
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