Yazar "Sarac, Esra" seçeneğine göre listele

Listeleniyor 1 - 7 / 7
  • [ X ]
    Öğe
    Applying data mining techniques to predict vitamin D deficiency in diabetic patients
    (Sage Publications Inc, 2023) Essiz, Ugur Engin; Yuregir, Oya Hacire; Sarac, Esra
    Vitamin D is among the vitamins necessary for both adults' and children's health. It plays a significant role in calcium absorption, the immune system, cell proliferation and differentiation, bone protection, skeletal health, rickets, muscle health, heart health, disease pathogenesis and severity, glucose metabolism, glucose intolerance, varying insulin secretion, and diabetes. Because the 25-hydroxyvitamin D (25OHD) test, which is used to measure vitamin D is expensive and may not be covered in healthcare benefits in many countries, this study aims to predict vitamin D deficiency in diabetic patients. The prediction method is based on data mining techniques combined with feature selection by using historical electronic health records. The results were compared with a filter-based feature selection algorithm, namely relief-F. Non-valuable features were eliminated effectively with the relief-F feature selection method without any performance loss in classification. The performances of the methods were evaluated using classification accuracy (ACC), sensitivity, specificity, F1-score, precision, kappa results, and receiver operating characteristic (ROC) curves. The analyses have been conducted on a vitamin D dataset of diabetic patients and the results show that the highest classification accuracy of 97.044% was obtained for the support vector machines (SVM) model using radial kernel that contains 18 features.
  • [ X ]
    Öğe
    Automated Depression Detection from Tweets: A Comparison of NLP Techniques
    (Institute of Electrical and Electronics Engineers Inc., 2024) Balci, Emirhan; Sarac, Esra
    This paper aims to classify suicidal ideation as a symptom of depression from social media posts by applying the state-of-the-art classification model BERT (Bidirectional Encoder Representations from Transformers) and three traditional machine learning algorithms for binary classification. Since depression is one of the most prevalent mental health disorders amongst psychiatric disorders, the authors intended to present an experimental analysis of the machine learning classifier results as a comparison of novel depression detection techniques. We utilized undiagnosed user posts from Twitter as our dataset and tested the fine-tuned BERT model by applying hold-out and 10-fold cross-validation techniques. Since the dataset is highly unbalanced, Support Vector Machine (SVM), Naive Bayes, and Random Forest algorithms were employed on the same dataset with and without the oversampling method SMOTE (Synthetic Minority Oversampling Technique). The results demonstrate that traditional machine learning classifiers cannot infer sentiment from data containing various linguistic cues, such as depression symptoms. On the other hand, the state-of-the-art model BERT achieves 99.29% and 99.56% macro and micro-F-measure values, respectively, surpassing traditional machine learning algorithms in terms of these metrics. As a robust solution to depression detection from textual data, the BERT model is more trustworthy than the traditional machine learning classifiers to detect specific cues related to depression and similar mental disorders. This study contributes to the relevant research areas of natural language processing by indicating the performance difference between the BERT model and several traditional machine learning algorithms as a generalized approach for classification tasks. © 2024 IEEE.
  • [ X ]
    Öğe
    Comparison of Feature Selection Methods for Sentiment Analysis on Turkish Twitter Data
    (IEEE, 2017) Parlar, Tuba; Sarac, Esra; Ozel, Selma Ayse
    The Internet and social media provide a major source of information about people's opinions. Due to the rapidly growing number of online documents, it becomes both time-consuming and hard task to obtain and analyze the desired opinionated information. Sentiment analysis is the classification of sentiments expressed in documents. To improve classification perfromance feature selection methods which help to identify the most valuable features are generally applied. In this paper, we compare the performance of four feature selection methods namely Chi-square, Information Gain, Query Expansion Ranking, and Ant Colony Optimization using Maximum Entropi Modeling classification algorithm over Turkish Twitter dataset. Therefore, the effects of feature selection methods over the performance of sentiment analysis of Turkish Twitter data are evaluated. Experimental results show that Query Expansion Ranking and Ant Colony Optimization methods outperform other traditional feature selection methods for sentiment analysis.
  • [ X ]
    Öğe
    Comparison of feature selection methods for sentiment analysis on Turkish Twitter data
    (Institute of Electrical and Electronics Engineers Inc., 2017) Parlar, Tuba; Sarac, Esra; Ozel, Selma Ayse
    The Internet and social media provide a major source of information about people's opinions. Due to the rapidly growing number of online documents, it becomes both time-consuming and hard task to obtain and analyze the desired opinionated information. Sentiment analysis is the classification of sentiments expressed in documents. To improve classification perfromance feature selection methods which help to identify the most valuable features are generally applied. In this paper, we compare the performance of four feature selection methods namely Chi-square, Information Gain, Query Expansion Ranking, and Ant Colony Optimization using Maximum Entropi Modeling classification algorithm over Turkish Twitter dataset. Therefore, the effects of feature selection methods over the performance of sentiment analysis of Turkish Twitter data are evaluated. Experimental results show that Query Expansion Ranking and Ant Colony Optimization methods outperform other traditional feature selection methods for sentiment analysis. © 2017 IEEE.
  • [ X ]
    Öğe
    Deep Learning-Based Prediction Models for theDetection of Vitamin D Deficiency and25-Hydroxyvitamin D Levels Using Complete BloodCount Tests
    (Editura Acad Romane, 2024) Essiz, Ugur Engin; Aci, Cigdem Inan; Sarac, Esra; Aci, Mehmet
    Vitamin D (VitD) is an essential nutrient that is critical for the well-being of both adults and children, and its deficiency is recognized as a precursor to several diseases. In previous studies, researchers have approached the problem of detecting vitamin D deficiency (VDD) as a single sufficient/deficient classification problem using machine learning or statistics-based methods. The main objective of this paper is to predict a patient's VitD status (i.e., sufficiency, insufficiency, or deficiency), severity of VDD (i.e., mild, moderate, or severe), and 25-hydroxyvitamin D (25(OH)D) level in a separate deep learning (DL)-based models. An original dataset consisting of complete blood count (CBC) tests from 907 patients, including 25(OH)D concentrations, collected from a public health laboratory was used for this purpose. CNN, RNN, LSTM, GRU and Auto-encoder algorithms were used to develop DL-based models. The top 25 features in the CBC tests were carefully selected by implementing the Extra Trees Classifier and Multi-task LASSO feature selection algorithms. The performance of the models was evaluated using metrics such as accuracy, F1-score, mean absolute error, root mean square error and R-squared. Remarkably, all three models showed satisfactory results when compared to the existing literature; however, the CNN-based prediction models proved to be the most successful.
  • [ X ]
    Öğe
    Detection of Cyberbullying on Social Media Messages in Turkish
    (IEEE, 2017) Ozel, Selma Ayse; Sarac, Esra; Akdemir, Seyran; Aksu, Hulya
    The increased use of the Internet and the ease of access to online communities like social media have provided an avenue for cybercrimes. Cyberbullying, which is a kind of cybercrime, is defined as an aggressive, intentional action against a defenseless person by using the Internet, social media, or other electronic contents. Researchers have found that many of the bullying cases have tragically ended in suicides; hence automatic detection of cyberbullying has become important. The aim of this study is to detect cyberbullying on social media messages written in Turkish. To our knowledge, this is the first study which makes cyberbully detection on Turkish texts. We prepare a dataset from Instagram and Twitter messages written in Turkish and then we applied machine learning techniques that are Support Vector Machines (SVM), decision tree (C4.5), Naive Bayes Multinomial, and k Nearest Neighbors (kNN) classifiers to detect cyberbullying. We also apply information gain and chi-square feature selection methods to improve the accuracy of classifiers. We observe that when both words and emoticons in the text messages are taken into account as features, cyberbully detection improves. Among the classifiers, Naive Bayes Multinomial is the most successful one in terms both classification accuracy and running time. When feature selection is applied classification accuracy improves up to 84% for the dataset used.
  • [ X ]
    Öğe
    IWD Based Feature Selection Algorithm for Sentiment Analysis
    (Kaunas Univ Technology, 2019) Parlar, Tuba; Sarac, Esra
    Feature selection methods aim to improve the classification performance by eliminating non-valuable features. In this paper, our aim is to apply a recent optimization technique namely the Intelligent Water Drops (IWD) algorithm to select best features for sentiment analysis. We investigate the classification performances of our proposed IWD based feature selection method by comparing one of the well-known feature selection method using Maximum Entropy classifier. Experimental results show that Intelligent Water Drops based feature selection method outperforms than ReliefF method for sentiment analysis.