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    Heart disease classification using neural network and feature selection
    (2011-11-04)
    Khemphila, Anchana
    ;
    Boonjing, Veera
    In this study, we introduces a classification approach using Multi-Layer Perceptron (MLP)with Back-Propagation learning algorithm and a feature selection algorithm along with biomedical test values to diagnose heart disease. Clinical diagnosis is done mostly by doctor's expertise and experience. But still cases are reported of wrong diagnosis and treatment. Patients are asked to take number of tests for diagnosis. In many cases, not all the tests contribute towards effective diagnosis of a disease. Our work is to classify the presence of heart disease with reduced number of attributes. Original, 13 attributes are involved in classify the heart disease. We use Information Gain to determine the attributes which reduces the number of attributes which is need to be taken from patients. The Artificial neural networks is used to classify the diagnosis of patients. Thirteen attributes are reduced to 8 attributes. The accuracy differs between 13 features and 8 features in training data set is 1.1% and in the validation data set is 0.82%. © 2011 IEEE.
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    Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients
    (2010-12-01)
    Khemphila, Anchana
    ;
    Boonjing, Veera
    In this study, performances of classification techniques were compared in order to predict the presence of the patients getting a heart disease.A retrospective analysis was performed in 303 subjects.We compared the performance of logistic regression(LR),decision trees(DTs) , and Artificial neural networks (ANNs).The variables were medical profiles are age,Sex,Chest Pain Type,Blood Pressure,Cholesterol,Fasting Blood Sugar, Resting ECG,Maximum Heart Rate,Induced Angina,Ole Peak,Slope,Number Colored Vessels,Thal and Concept Class.We have created the model using logistic regression classifiers , artificial neural networks and decision trees that they are often used for classification problems.Performances of classification techniques were compared using lift chart and error rates.In the result, artificial neural networks have the greatest area between the model curve and the baseline curve.The error rates are 0.22,0.198,0.21,respectively for logistic regression , artificial neural networks and decision trees.The neural networks exhibited sensitivity of 81.1% , specificity of 78.7% and accuracy of 80.2%,while the decision tree provided the prediction performance with a sensitivity, specificity and accuracy of 81.7%,76.0% and 79.3%.And the logistic regression provided the prediction performance with a sensitivity,specificity and accuracy of 81.2%,73.1% and 77.7% Artificial neural networks have the least of error rate and has the highest accuracy ,therefore Artificial neural networks is the best technique to classify in this data set. ©2010 IEEE.
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    Ultra Wideband network in short-range wireless system for personal computer
    (2006-12-01)
    Khemphila, Anchana
    ;
    Promwong, Sathaporn
    In this paper, we proposed the Ultra Wideband (UWB) for personal communication system. The definition of UWB signals released by the Federal Communications Commission (FCC) opened the way to both impulse and non-impulse UWB signal formats, as reflected within the IEEE 802.15.3a TG, devoted to the definition of a standard for UWB-based high bit rate WPANs. Nowadays wireless communication is widely used efficiently in high-speed data transferring method. So the factors that effected UWB system are required to known. In this research the small distance of wireless system are observed. The experiment frequency from 3-11 GHz and the transfer function of the transitter (TX) and receiver (RX) antennas are done using biconical antennas. The measurements are covered by using a vector network analyzer (VNA) and data are used to evaluate the UWB transmission properties based on the extension Friis's transmission formula. The relative gain, power delay profile and path loss of signals turn on and turn off computer network at Tx and Rx antennas are shown. © 2006 IEEE.