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Alickovic, Emina
Publications (3 of 3) Show all publications
Subasi, A., Alickovic, E. & Kevric, J. (2017). Diagnosis of Chronic Kidney Disease by Using Random Forest. In: : . Paper presented at 2nd Conference of Medical and Biological Engineering in Bosnia and Herzegovina (CMBEBIH 2017) , Sarajevo, March 16-18, 2017 (pp. 589-594). Springer
Open this publication in new window or tab >>Diagnosis of Chronic Kidney Disease by Using Random Forest
2017 (Swedish)Conference paper, Published paper (Refereed)
Abstract [en]

Chronic kidney disease (CKD) is a global public health problem, affecting approximately 10% of the population worldwide. Yet, there is little direct evidence on how CKD can be diagnosed in a systematic and automatic manner. This paper investigates how CKD can be diagnosed by using machine learning (ML) techniques. ML algorithms have been a driving force in detection of abnormalities in different physiological data, and are, with a great success, employed in different classification tasks. In the present study, a number of different ML classifiers are experimentally validated to a real data set, taken from the UCI Machine Learning Repository, and our findings are compared with the findings reported in the recent literature. The results are quantitatively and qualitatively discussed and our findings reveal that the random forest (RF) classifier achieves the near-optimal performances on the identification of CKD subjects. Hence, we show that ML algorithms serve important function in diagnosis of CKD, with satisfactory robustness, and our findings suggest that RF can also be utilized for the diagnosis of similar diseases.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Chronic kidney disease (CKD), Machine learning, Artificial Neural Networks (ANNs), Support Vector Machines (SVM), k-Nearest Neighbour (k-NN), C4.5 Decision Tree Random Forest (RF)
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-135782 (URN)10.1007/978-981-10-4166-2_89 (DOI)978-981-10-4165-5 (ISBN)
Conference
2nd Conference of Medical and Biological Engineering in Bosnia and Herzegovina (CMBEBIH 2017) , Sarajevo, March 16-18, 2017
Available from: 2017-03-22 Created: 2017-03-22 Last updated: 2019-01-31Bibliographically approved
Alickovic, E., Lunner, T. & Gustafsson, F. (2016). A System Identification Approach to Determining Listening Attention from EEG Signals. In: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO): . Paper presented at 24th European Signal Processing Conference (EUSIPCO), Aug 28-Sep 2, 2016. Budapest, Hungary (pp. 31-35). IEEE
Open this publication in new window or tab >>A System Identification Approach to Determining Listening Attention from EEG Signals
2016 (English)In: 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE , 2016, p. 31-35Conference paper, Published paper (Refereed)
Abstract [en]

We still have very little knowledge about how ourbrains decouple different sound sources, which is known assolving the cocktail party problem. Several approaches; includingERP, time-frequency analysis and, more recently, regression andstimulus reconstruction approaches; have been suggested forsolving this problem. In this work, we study the problem ofcorrelating of EEG signals to different sets of sound sources withthe goal of identifying the single source to which the listener isattending. Here, we propose a method for finding the number ofparameters needed in a regression model to avoid overlearning,which is necessary for determining the attended sound sourcewith high confidence in order to solve the cocktail party problem.

Place, publisher, year, edition, pages
IEEE, 2016
Series
European Signal Processing Conference, ISSN 2076-1465
Keywords
attention, cocktail party, linear regression (LR), finite impulse response (FIR), multivariable model, sound, EEG
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-130732 (URN)10.1109/EUSIPCO.2016.7760204 (DOI)000391891900007 ()978-0-9928-6265-7 (ISBN)978-1-5090-1891-8 (ISBN)
Conference
24th European Signal Processing Conference (EUSIPCO), Aug 28-Sep 2, 2016. Budapest, Hungary
Available from: 2016-08-22 Created: 2016-08-22 Last updated: 2017-02-15
Alickovic, E. & Subasi, A. (2016). Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier. Journal of medical systems, 40(4), 108
Open this publication in new window or tab >>Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests Classifier
2016 (English)In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 40, no 4, p. 108-Article in journal (Refereed) Published
Abstract [en]

In this study, Random Forests (RF) classifier is proposed for ECG heartbeat signal classification in diagnosis of heart arrhythmia. Discrete wavelet transform (DWT) is used to decompose ECG signals into different successive frequency bands. A set of different statistical features were extracted from the obtained frequency bands to denote the distribution of wavelet coefficients. This study shows that RF classifier achieves superior performances compared to other decision tree methods using 10-fold cross-validation for the ECG datasets and the obtained results suggest that further significant improvements in terms of classification accuracy can be accomplished by the proposed classification system. Accurate ECG signal classification is the major requirement for detection of all arrhythmia types. Performances of the proposed system have been evaluated on two different databases, namely MIT-BIH database and St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database. For MIT-BIH database, RF classifier yielded an overall accuracy 99.33 % against 98.44 and 98.67 % for the C4.5 and CART classifiers, respectively. For St. -Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, RF classifier yielded an overall accuracy 99.95 % against 99.80 % for both C4.5 and CART classifiers, respectively. The combined model with multiscale principal component analysis (MSPCA) de-noising, discrete wavelet transform (DWT) and RF classifier also achieves better performance with the area under the receiver operating characteristic (ROC) curve (AUC) and F- measure equal to 0.999 and 0.993 for MIT-BIH database and 1 and 0.999 for and St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database, respectively. Obtained results demonstrate that the proposed system has capacity for reliable classification of ECG signals, and to assist the clinicians for making an accurate diagnosis of cardiovascular disorders (CVDs).

Place, publisher, year, edition, pages
SPRINGER, 2016
Keywords
Electrocardiogram (ECG); Multiscale Principal Component Analysis (MSPCA); Discrete Wavelet Transform (DWT); Decision Tree; Random Forest (RF); Heart arrhythmia
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:liu:diva-126802 (URN)10.1007/s10916-016-0467-8 (DOI)000371468500023 ()26922592 (PubMedID)
Available from: 2016-04-07 Created: 2016-04-05 Last updated: 2017-11-30
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