Türkiye’de Kentleşme Süreci ve İllerin GSYH Verileri ile Göç Oranları Arasındaki İlişkinin Kümeleme Analiziyle İncelenmesi
Süleyman Demirel Üniversitesi Vizyoner Dergisi
Migration is a concept as old as human history. Large-scale migrations have occurred in every per... more Migration is a concept as old as human history. Large-scale migrations have occurred in every period of human history. There are many economic, social, political and legal reasons behind migration movements. It is known that economic reasons are the most determining factors in the occurrence of migration. Generally, the direction of migration is from rural to urban, underdeveloped regions to developed regions, east to west in Turkey. Population is largely concentrated in cities. Cities with a high level of economic development are the cities with the most populous population. While many socio-economic indicators can be used to reveal the level of economic development, an evaluation can also be made based on the GDP data of the provinces, which is considered as a combination of these indicators. In the study, a statistical significance relationship is sought between migration data and GDP data of the provinces. The analysis method used is the cluster analysis. According to the result...
Assessment of Association Rule Mining Using Interest Measures on the Gene Data
Medical Records
Aim: Data mining is the discovery process of beneficial information, not revealed from large-scal... more Aim: Data mining is the discovery process of beneficial information, not revealed from large-scale data beforehand. One of the fields in which data mining is widely used is health. With data mining, the diagnosis and treatment of the disease and the risk factors affecting the disease can be determined quickly. Association rules are one of the data mining techniques. The aim of this study is to determine patient profiles by obtaining strong association rules with the apriori algorithm, which is one of the association rule algorithms. Material and Method: The data set used in the study consists of 205 acute myocardial infarction (AMI) patients. The patients have also carried the genotype of the FNDC5 (rs3480, rs726344, rs16835198) polymorphisms. Support and confidence measures are used to evaluate the rules obtained in the Apriori algorithm. The rules obtained by these measures are correct but not strong. Therefore, interest measures are used, besides two basic measures, with the aim ...
Breast cancer, which is an important public health problem worldwide, is one of the deadliest can... more Breast cancer, which is an important public health problem worldwide, is one of the deadliest cancers in women. This study aims to classify open-access breast cancer data and identify important risk factors with the Stochastic Gradient Boosting Method. The open-access breast cancer dataset was used to construct a classification model in the study. Stochastic Gradient Boosting was used to classify the disease. Balanced accuracy, accuracy, sensitivity, specificity, and positive/negative predictive values were evaluated for model performance. The accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score metrics obtained with the Stochastic Gradient Boosting model were 100 %, 100 %, 100 %, 100 %, 100 %, and 100 %, and 100 % respectively. In addition, the importance of the variables obtained, the most important risk factors for breast cancer were a cave. points_mean, area_worst, and perimeter_worst, concave. points_worst respectively. According to the study results, with the machine-learning model Stochastic Gradient Boosting used, patients with and without breast cancer were classified with high accuracy, and the importance of the variables related to cancer status was determined. Factors with high variable importance can be considered potential risk factors associated with cancer status and can play an essential role in disease diagnosis.
Early Diagnosis of Diabetes Mellitus by Machine Learning Methods According to Plasma Glucose Concentration, Serum Insulin Resistance and Diastolic Blood Pressure Indicators
Assessment of Association Rules based on Certainty Factor: an Application on Heart Data Set
2019 International Artificial Intelligence and Data Processing Symposium (IDAP), 2019
Association rules mining is one of the uttermost applied techniques in data mining and artificial... more Association rules mining is one of the uttermost applied techniques in data mining and artificial intelligence. Support and confidence are two basic measures employed in the evaluation of association rules. The rules obtained with these two values are often correct; however, they are not strong rules. Most of the rules, especially with a high support value, are misleading. For this reason, there are many interestingness measures proposed to achieve stronger rules. In this study it is aimed to establish strong association rules with variables in open sourced heart data set. In the current study, Apriori algorithm was used to obtain the rules. As a result of the analysis, only 55 confidence and support criteria were taken into consideration. For more powerful rules, certainty factor was used as one of the interestingness measure proposed in the literature, and it was concluded that only 26 of these rules were strong. As a result of the analysis of the findings obtained in the context ...
Toplumun cekirdegini olusturan aile kurumu, cocuk acisindan ilk ve en onemli sosyal cevredir. Coc... more Toplumun cekirdegini olusturan aile kurumu, cocuk acisindan ilk ve en onemli sosyal cevredir. Cocuklarin gelisim surecinin en etkili unsuru olarak aile kurumunun islevini yerine getirememesi nedeniyle, cocuklarin ihtiyac duydugu ebeveyn desteginden mahrum kalmasi, cocuklar uzerinde olumsuz etki yapmaktadir. Bu durumdan hareketle calismada, bosanma, ayri yasama vb olaylarin cocuklarin suca suruklenmesi uzerindeki etkisi incelenmistir. 2011-2015 yillarina iliskin resmi istatistiklerinden elde edilen verilerin kullanildigi calisma kapsaminda, Turkiye’deki kaba evlenme hizi verileri, kaba bosanma hizi verileri ile suca suruklenen cocuklara ait veriler istatistiksel olarak degerlendirilmistir. Kaba evlenme hizi ile kaba bosanma hizi arasindaki iliskinin belirlenmesi amaciyla yapilan korelasyon analizi sonucunda, negatif yonde anlamli bir iliski tespit edilmistir. Suca suruklenen cocuklar icerisinden ailesi ile birlikte yasayanlar ve digerleri olmak uzere olusturulan iki grubun ortalamala...
Sample Size Effect on Classification Performance of Machine Learning Models: An Application of Coronary Artery Disease
Cardiovascular diseases are among the most common causes of death due to their widespread prevale... more Cardiovascular diseases are among the most common causes of death due to their widespread prevalence. Accurate and timely diagnosis of coronary artery disease, one of the fatal cardiovascular diseases, is very important. Angiography, an invasive method, is an expensive and special method used to determine the disease and can cause serious complications. Therefore, cheaper and more efficient data mining methods are used in the diagnosis and treatment of cardiovascular diseases. As an alternative approach, by establishing clinical decision support systems using data modeling and analysis methods such as data mining, errors and costs can be reduced by providing clinicians with computer-aided diagnosis, and patient safety and clinical decision quality can be significantly increased. In this study, the data set on the open-source access website was used to classify cardiovascular disease and consists of patient records of 14 variables created by the Cleveland clinic. Also, machine learni...
Data mining is the process of discovering useful information that has not been previously reveale... more Data mining is the process of discovering useful information that has not been previously revealed from large amounts of data. Association rules mining is one of the most important techniques used in data mining and artificial intelligence. The first research in the association rules was to find relationships between different products in the customer transaction database and customer purchase models. Based on these relationships, researchers have begun to expand the field of data mining. One of these areas is the application of the rules of association in the field of medicine. Thus, through these applications, the relationship of various features in medical data can be discovered, and the findings obtained can aid medical diagnosis. Support and confidence are the two primary measures employed in the evaluation of association rules. The rules obtained with these two values are often correct ; however, they are not strong rules. For this reason, there are many interestingness measur...
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