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    A diagnostic fuzzy rule-based system for congenital heart disease
    (2011) Kaya E.; Oran B.; Arslan A.
    In this study, fuzzy rule-based classifier is used for the diagnosis of congenital heart disease. Congenital heart diseases are defined as structural or functional heart disease. Medical data sets were obtained from Pediatric Cardiology Department at Selcuk University, from years 2000 to 2003. Firstly, fuzzy rules were generated by using medical data. Then the weights of fuzzy rules were calculated. Two different reasoning methods as "weighted vote method" and "singles winner method" were used in this study. The results of fuzzy classifiers were compared.
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    Earning weights of fuzzy rules by using gravitational search algorithm
    (2013) Kaya E.; Koçer B.; Arslan A.
    The fuzzy rules are the most important factor which affects the success in fuzzy rule-based systems. Performance of a fuzzy rule-based classifier can be improved by weighting fuzzy rules. There are different methods for weighting fuzzy rules. In this study, weights of fuzzy rules which are generated from datasets in fuzzy rule-based classifiers are determined by using Gravitational Search Algorithm. Gravitational search algorithm is a new algorithm for optimization problems. The aim of the GSA in the proposed method is to find rule weight values which maximize classification accuracy. The proposed method was tested with six different data sets. The simulation results are compared by the other method. The proposed method mostly provided better results than the other method compa red. © 2013 ICIC International.
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    A rough sets approach for diagnostic M-mode evaluation in newborn with congenital heart diseases
    (2010) Kaya E.; Oran B.; Arslan A.
    This paper presents a methodology based on rough set theory that can be used to identify those newborn with congenital heart diseases for which performing the test is redundant or superfluous. Pawlak introduced rough set theory in the early 1980s as a tool for representing and reasoning about imprecise or uncertain information. Our method depends on cardiac morphologic measurement algorithms for diagnosis template for neonatal congenital heart diseases accuracy. In this study, relationship between cardiac morphologic measurement and diagnosis of neonatal congenital heart diseases is analysed. Examples of echocardiographic M-mode values application for prediction of diagnosis for newborn infant with congenital heart diseases are given. Furthermore, the methodology enables one to automatically construct a set of descriptive and minimal if-then rules that model the patient group in need of the test. A reanalysis of a previously published real-world dataset of patients with normal cardiovascular system is used as this study. This may be helpful in supporting decisions concerning diagnosis of congenital heart diseases in newborn babies using rough set techniques. In addition, medical rough set techniques can discover novel diagnostic facility and associations that are useful to paediatric cardiologists. © 2010 IEEE.
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    Science anxiety among failing students [Başari{dotless}si{dotless}z ö?renciler arasi{dotless}ndaki fen kaygi{dotless}si{dotless}]
    (Ankara University, 2014) Kaya E.; Yildirim A.
    This study aimed to investigate sources of failing students' science anxiety. The sample, chosen through criterion sampling, was composed of six ninth-grade students who failed at chemistry course. Semi-structured interviews were carried out with these students. The interview questions were related to students' perceptions, experiences and feelings in relation to chemistry courses. During the interviews, a tape recorder was used to gather and record data. Data were subjected to content analysis in which the data were first assigned relevant codes and then themes were determined based these codes. The findings indicated that the students' science anxiety resulted from unpleasant classroom activities, fear of test, perceptions of chemistry, teacher attitudes, and parent attitudes.