Feature Selection and Classification of Leukemia Cancer Using Machine Learning Techniques
Md. Alamgir Sarder,
Md. Maniruzzaman,
Benojir Ahammed
Issue:
Volume 5, Issue 2, June 2020
Pages:
18-27
Received:
26 February 2020
Accepted:
12 June 2020
Published:
4 July 2020
Abstract: Leukemia cancer is one of the most leading detrimental cancer diseases in worldwide. A huge number of genes are responsible for cancer diseases. Therefore, it is necessary to identify the most informative genes of Leukemia cancer. The main objectives of this study are to: (i) identify the most informative genes using five feature selection techniques (FST) and (ii) adopt six classifiers to classify the cancer disease and compare them. Leukemia cancer data has been taken from Kent ridge biomedical data repository, USA. There are 7129 genes and 72 patients. Among them, 47 patients are cancer and 25 are control. We have used five FST as t-test; Wilcoxon sign rank sum (WCSRS) test, random forest (RF), Boruta and least absolute shrinkage and selection operator (LASSO). We have also used six classifiers as Adaboost (AB), classification and regression tree (CART), artificial neural network (ANN), random forest (RF), linear discriminant analysis (LDA) and naive Bayes (NB). The performances of these classifiers are evaluated by accuracy (ACC), sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and F-measure (FM). We used simulated dataset to check the validity of proposed method. The results indicate that the combination of LASSO based FST and NB classifier gives the highest classification accuracy of 99.95%. On the basis of the results, we can conclude that the combination of LASSO based FST and NB classifier predicts the leukemia cancer more accurately compare to any other combination of FST and classifiers utilized in this study.
Abstract: Leukemia cancer is one of the most leading detrimental cancer diseases in worldwide. A huge number of genes are responsible for cancer diseases. Therefore, it is necessary to identify the most informative genes of Leukemia cancer. The main objectives of this study are to: (i) identify the most informative genes using five feature selection techniqu...
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Diagnosis of Epilepsy Using Signal Time Domain Specifications and SVM Neural Network
Simin Mirzayi,
Saman Rajebi
Issue:
Volume 5, Issue 2, June 2020
Pages:
28-38
Received:
10 September 2020
Accepted:
27 September 2020
Published:
7 October 2020
Abstract: Epilepsy is a central nervous system (neurological) disorder that is caused by abnormal pathologic oscillating activity of a group of nerve cells in the brain. The electroencephalographic signals gained from brain electrical activities are mostly used for the diagnosis of neurological diseases. These signals indicate electrical activities in the brain and they contain some data about the brain; however, gaining long-term EEG data with seizure activities specifically in regions lacking medical centers and educated neurologists would be very costly and unpleasant. In this article based on electroencephalogram (EEG) signals, a new method is proposed for the automatic detection of Epilepsy. The aim of this article is to provide a model for the detection of Epilepsy by SVM optimization using genetic algorithm for the classification of EEG data. SVMs are one the powerful technics of machine learning, and they are widely applicable in many fields. The training and testing data were obtained from investigating EEG signals of 367 healthy and ill individuals. The data used in this paper have been derived from Barekat Imam Khomeini (RAH) Hospital in Miyaneh city. In this study the noise removal was done over the data by FIR Filter and genetic algorithm was used for the calculation of filter coefficients and optimal sample number. This method classifies the signals of both healthy individuals and the ones with Epilepsy with an accuracy of 100%.
Abstract: Epilepsy is a central nervous system (neurological) disorder that is caused by abnormal pathologic oscillating activity of a group of nerve cells in the brain. The electroencephalographic signals gained from brain electrical activities are mostly used for the diagnosis of neurological diseases. These signals indicate electrical activities in the br...
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