Yazar "Shaker, Khalid" seçeneğine göre listele

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    Öğe
    A hybrid classification algorithm approach for breast cancer diagnosis
    (Ieee, 2016) Abed, Baraa M.; Shaker, Khalid; Jalab, Hamid A.; Shaker, Hothefa; Mansoor, Ali Mohammed; Alwan, Ahmad F.; Al-Gburi, Ihsan Salman
    Early diagnosis of Breast Cancer is significantly important to treat the disease easily therefore it is necessary to develop techniques that can help physicians to get accurate diagnosis. This study suggests a hybrid classification algorithm which is based upon Genetic Algorithm (GA) and k Nearest neighbor algorithm (kNN). GA algorithm has been used for its primary purpose as an optimization technique for kNN by selecting best features as well as optimization of the k value, while the kNN is used for classification purpose. The planned algorithm is tested by applying it on Wisconsin Breast Cancer Dataset from UCI Repository of Machine Learning Databases using different datasets in which the first is Wisconsin Breast Cancer Database (WBCD) and the second one is Wisconsin Diagnosis Breast Cancer (WDBC) which has changes in the number of attributes and number of instances. The proposed algorithm was measured against different classifier algorithms on the same database. The evaluation results of the algorithm proposed have achieved 99% accuracy.
  • [ X ]
    Öğe
    Evaluation and measuring classifiers of diabetes diseases
    (Ieee, 2017) Jasim, Ihsan Salman; Duru, Adil Deniz; Shaker, Khalid; Abed, Baraa M.; Saleh, Hadeel M.
    Classification plays tremendous role in data mining process, especially for huge amount of data and it is suitable for predict new knowledge and discover patterns. This process can work with different types of data whether it was nominal or continuous. In this paper classification will be performs on diseases diagnoses by choosing to work with (k-nearest neighborhood algorithm KNN) measure and evaluate the method with (Artificial Neural Network ANN). These two classification methods have been chosen to classify (Pima-Indian-Diabetes PID) using spiral spinning technique. Classification done by taking 1 to 50 values of (K) in KNN versus 1 to 50 values of hidden layers for ANN in single iteration checking the accuracy as measuring to evaluate performance. T-test used to validate choosing two different factors (K in KNN and number of hidden layers in ANN), t-test results shows that the method is extremely statically significant. After performing classification by changing architecture, ANN proves better results than KNN in this disease classification.
  • Küçük Resim
    Öğe
    Impact of metaheuristic iteration on artificial neural network structure in medical data
    (Mdpi, 2018) Salman, İhsan; Uçan, Osman Nuri; Bayat, Oğuz; Shaker, Khalid
    Medical data classification is an important factor in improving diagnosis and treatment and can assist physicians in making decisions about serious diseases by collecting symptoms and medical analyses. In this work, hybrid classification optimization methods such as Genetic Algorithm (GA), Particle Swam Optimization (PSO), and Fireworks Algorithm (FWA), are proposed for enhancing the classification accuracy of the Artificial Neural Network (ANN). The enhancement process is tested through two experiments. First, the proposed algorithms are applied on five benchmark medical data sets from the repository of the University of California in Irvine (UCI). The model with the best results is then used in the second experiment, which focuses on tuning the parameters of the selected algorithm by choosing a different number of iterations in ANNs with different numbers of hidden layers. Enhanced ANN with the three optimization algorithms are tested on biological gene sequence big dataset obtained from The Cancer Genome Atlas (TCGA) repository. GA and FWA are statistically significant but PSO was statistically not, and GA overcame PSO and FWA in performance. The methodology is successful and registers improvements in every step, as significant results are obtained.