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  • Küçük Resim Yok
    Öğe
    Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine
    (2007) Polat K.; Güneş S.
    Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. The ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this study, we have detected on ECG Arrhythmias using principal component analysis (PCA) and least square support vector machine (LS-SVM). The approach system has two stages. In the first stage, dimension of ECG Arrhythmias dataset that has 279 features is reduced to 15 features using principal component analysis. In the second stage, diagnosis of ECG Arrhythmias was conducted by using LS-SVM classifier. We took the ECG Arrhythmias dataset used in our study from the UCI (from University of California, Department of Information and Computer Science) machine learning database. Classifier system consists of three stages: 50-50% of training-test dataset, 70-30% of training-test dataset and 80-20% of training-test dataset, subsequently, the obtained classification accuracies; 96.86%, 100% ve 100%. The end benefit would be to assist the physician to make the final decision without hesitation. This result is for ECG Arrhythmias disease but it states that this method can be used confidently for other medical diseases diagnosis problems, too. © 2006 Elsevier Inc. All rights reserved.
  • Küçük Resim
    Öğe
    A new classification method to diagnosis liver disorders: Supervised Artificial Immune System (AIRS) [Karaci?er rahatsizli?i teşhisinde yeni bir siniflama yöntemi: Danişmali Yapay Ba?işiklik Sistemi (AIRS)]
    (2005) Polat K.; Şahan S.; Kodaz H.; Güneş S.
    Medical diagnosis is very important in medicine. It is necessary to form an efficient and effective computer-based method for decision support in medical analysis. Artificial Immune Systems (AIS), which we can say very new, is an effective and prosperous artificial intelligence area with respect to its problem solving performance. In the beginning, it was formed for helping medical experts to understand the working procedure of immune system in more detail by modeling interactions in immune system. The used medical data was taken from machine learning database of California University in Irvine. In this study, each of data was classified with Artificial Immune Recognition System (AIRS). This application was done by using MATLAB 6.5 programming language. AIRS classification algorithm, which has an important place among classification algorithms in the field of Artificial Immune Systems, has showed an effective and intriguing performance on the problem it was applied. AIRS was previously applied to some medical classification problems including Breast Cancer, Cleverand Heart Disease, Diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. © 2005 IEEE.