Elif Kartal

Bio: Elif Kartal is an academic researcher from Istanbul University. The author has contributed to research in topics: Random forest & Learning Management. The author has an hindex of 4, co-authored 21 publications receiving 51 citations. Previous affiliations of Elif Kartal include Istanbul Technical University.

Classification of Mammography Images by Machine Learning Techniques

Abstract: Early diagnosis and accurate treatment is crucial in increasing the survival rate of diseases that can result in death, such as breast cancer. Therefore, there is a greater need for artificial intelligence systems that will help doctors make decisions in health care, especially in fatal diseases. Because these systems are not affected by human nature factors such as distraction, stress etc. so that they can distinguish small and important issues that could be overlooked, especially in the scan results of the patient. The aim of this study is to predict whether a mass can be identified in breast and whether the mass found in the breast is benign or malignant with the help of machine learning which is a sub-study area of artificial intelligence. In this study, the images in the mini-MIAS database are used. Firstly, unwanted areas were eliminated. Then Gauss, Average, Median and Wiener filters were applied to reduce noise and smoothing the images and an algorithm based on Contrast-Limited Adaptive Histogram Equalization (CLAHE) was applied to make suspicious areas more visible. New data sets were created by using HOG (Histogram of Oriented Gradients), LBP (Local Binary Pattern), GLCM (Gray Level Co-occurrence Matrices) for feature extraction and correlation (COR) for feature selection. Selected features were classified in three different categories (normal, benign, malignant) and two different categories (normal, abnormal) using. Different machine learning algorithms (C5.0 (normal and boosted), Naive Bayes, CART and Random Forest) were applied to the data sets and the performances were compared. According to the research findings, to decide whether there was a breast mass, the highest accuracy value was calculated by applying Median and Wiener filters together, equating histogram with CLAHE and using the GLCM feature extraction method on the data set and the accuracy was found 0.657 with Naive Bayes algorithm. When trying to find out whether the mass found in the breast is benign or malignant, Median was applied together with Weiner Filter, equating histogram with CLAHE and HOG feature extraction method was used, and the accuracy was calculated as 0,660 with Random Forest algorithm.

Machine learning techniques in cardiac risk assessment.

Abstract: Background The objective of this study was to predict the mortality risk of patients during or shortly after cardiac surgery by using machine learning techniques and their learning abilities from collected data. Methods The dataset was obtained from Acibadem Maslak Hospital. Risk factors of the European System for Cardiac Operative Risk Evaluation (EuroSCORE) were used to predict mortality risk. First, Standard EuroSCORE scores of patients were calculated and risk groups were determined, because 30-day follow-up information of patients was not available in the dataset. Models were created with five different machine learning algorithms and two different datasets including age, serum creatinine, left ventricular dysfunction, and pulmonary hypertension were numeric in Dataset 1 and categorical in Dataset 2. Model performance evaluation was performed with 10-fold cross-validation. Results Data analysis and performance evaluation were performed with R, RStudio and Shiny. C4.5 was selected as the best algorithm for risk prediction (accuracy= 0.989) in Dataset 1. This model indicated that pulmonary hypertension, recent myocardial infarct, surgery on thoracic aorta are the primary three risk factors that affect the mortality risk of patients during or shortly after cardiac surgery. Also, this model is used to develop a dynamic web application which is also accessible from mobile devices (https://elifkartal.shinyapps.io/euSCR/). Conclusion The C4.5 decision tree model was identified as having the highest performance in Dataset 1 in predicting the mortality risk of patients. Using the numerical values of the risk factors can be useful in increasing the performance of machine learning models. Development of hospital-specific local assessment systems using hospital data, such as the application in this study, would be beneficial for both patients and doctors.

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Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment

Abstract: In this research, monthly wind speed time series of the Kirsehir was investigated using the stand-alone, hybrid and ensemble models. The artificial neural networks, Gaussian process regression, support vector machines and multivariate adaptive regression splines were employed as stand-alone machine learning models, while the discrete wavelet transform was utilized as a pre-processing technique to create hybrid models. Moreover, for the first time in wind speed predictions, we generated a multi-stage ensemble model by using the M5 Model Tree (M5) algorithm to increase the model accuracies. Two major tasks considered to be necessary, in which the first is to obtain the lag times by using autocorrelation functions, and the latter is to determine the optimum mother wavelet as well as the decomposition level to reduce the uncertainties in wavelet modeling. The results revealed that the hybrid wavelet models outperformed the stand-alone models, while a significant improvement was also observed in M5 ensemble models as the highest Nash–Sutcliffe efficiency coefficient values were obtained in M5 hybrid wavelet multi-stage ensemble models for each lead time prediction. The findings of the study were assessed with respect to the various performance indicators and Kruskal–Wallis test to indicate whether the results are statically significant. The proposed multi-stage ensemble framework also benchmarked with the classical tree-based ensembles, such as Random forest, AdaBoost and XGBoost.