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Öğe A Decision Support System for Machine Learning-Based Determination of Zinc Deficiency: A Study in Adolescent Patients(Brieflands, 2024) Orbatu, Dilek; Bulgan, Zeynep Izem Peker; Olmez, Emre; Er, OrhanBackground: Over the past three years, zinc deficiency among adolescents has varied based on region and access to healthcare. Globally, zinc deficiency affects approximately 2 billion people, leading to serious issues such as immune problems and growth delays, particularly in developing countries. In the U.S., around 10% of adolescents experienced zinc deficiency in 2021, with a higher prevalence among teenage girls. In Europe, deficiency rates are generally low but can be significant in Eastern Europe and Central Asia. In Asia, particularly in rural and low-income areas, deficiency rates range from 20- 30%. In Turkey, the prevalence is high due to poor nutrition. Objectives: This study aimed to develop a machine learning-based decision support system to determine zinc deficiency in children and adolescents aged ID- 18 years. Methods: This machine learning-based study was conducted with 370 adolescents aged 10-18 years to assess their zinc deficiency. The dataset consists of 8 feature vectors and an output vector. The machine learning methods used in the analysis include logistic regression, naive bayes, decision tree (CART), K-nearest neighbors (K-NN), support vector machine (SVM), gradient boosting classifier, AdaBoost classifier; bagging classifier; random forest classifier; multilayer perceptron (MLP) classifier; and XGBoost (XGB) classifier. Evaluation metrics such as accuracy, precision, recall, and Fl score were used to assess the performance of these methods. Including specific values for these metrics, such as SVM achieved 94.6% accuracy, would allow readers to quicldy compare the effectiveness of the models. Different metrics serve various purposes: Accuracy provides an overall view of performance, precision and recall highlight specific aspects, and the Fl score balances precision and recall. Results: The mean age of the patients in the dataset was 13.79 +/- 1.18 years. Of the children, 6432% (n = 238) were female and 35.68% (n =B2) were male. It was found that 62.7% (n = 232) of the children had low zinc levels, while 373% (n = ox ) did not require zinc supplementation. Thirteen different machine learning methods were applied to a 70% training and 30% testing set. As a result, the SVM method provided the most successful outcome with 94.6% accuracy. Implementing the SVM-based system in pediatric clinics could improve efficiency and patient care by automatically detecting high-risk zinc deficiency patients based on lab results, providing early intervention alerts for faster treatment, and improving health outcomes. Highlighting these practical applications could increase the study's appeal to healthcare professionals by demonstrating its real-world benefits. Providing detailed information on these applications would enhance the study's clarity and practical value, making it more valuable for researchers and healthcare providers interested in Al tools for adolescent health. Conclusions: This study concluded that machine learning methods can effectively determine zinc deficiency in children. The SVM method demonstrated superior classification performance compared to the other methods. An SVM-based decision support system could be integrated into pediatric outpatient clinics to enhance diagnostic accuracy and patient care.Öğe A Decision Support System on Artificial Intelligence Based Early Diagnosis of Sepsis(2022) Kaya Aksoy, Pınar; Erdemir, Fatih; Kılınç, Deniz; Er, OrhanSepsis is the intense reaction of the immune system as a result of a severe infection in any part of the body and damages to organs and tissues. And this disease is commonly fatal and costly. In this study, we perform a comparative study for Sepsis prediction using machine learning algorithms from original laboratory findings. For this purpose, thirty-two different machine learning algorithms including different tructures as well as neural network classifiers are evaluated and compared. As a result of experimental studies, SVM (Cubic, Fine Gaussian), KNN (Fine, Weighted, Subspace), Trees (Weighted, Boosted, Bagged) and neural network-based classifiers have achieved a significant success rate in the diagnosis of Sepsis using the new dataset. Thus, it is concluded that it is appropriate to use machine learning algorithms to predict whether a Sepsis patient will be survived. This study has the potential to be used as a new supportive tool for doctors when predicting Sepsis.Öğe AFWDroid- Deep feature extraction and weighting for android malware detection(2021) Er, Orhan; Arslan, Recep Sinan; Ölmez, EmreAndroid malware detection is a critical and important problem that must be solved for a widely used operating system. Conventional machine learning techniques first extract some features from applications, then create classifiers to distinguish between malicious and benign applications. Most of the studies available today ignore the weighting of the obtained features. To overcome this problem, this study proposes a new software detection method based on weighting the data in feature vectors to be used in classification. To this end, firstly, the manifest file was read from the Android application package. Different features such as activities, services, permissions were extracted from the file, and for classification, a selection was made among these features. The parameters obtained as a result of selection were optimized by the deep neural network model. Studies revealed that through feature selection and weighting, better performance values could be achieved and more competitive results could be obtained in weight-sensitive classification.Öğe Artificial Intelligence Based Chatbot in E-Health System(İzmir Bakırçay Üniversitesi, 2023) Akarsu, Kamil; Er, OrhanThe healthcare sector is undergoing a digital revolution due to the rapid growth of technology, and AI technologies are becoming more commonplace in the sector. Chatbots have become useful resources for people to get advice and information about their health issues. The creation and implementation of an AI-based chatbot, integrated with an e-health system, is the main topic of this article. This paper explains the development and creation of chatbots. The chatbot's language comprehension and response capabilities are enhanced through the use of AI techniques such as machine learning and natural language processing (NLP). In addition, the chatbot's user interaction procedure and data security precautions are covered. The paper also examines how the developed chatbot can be integrated into an e-health platform and provides the results of user testing. These evaluations focus on the chatbot's ability to provide accurate and insightful answers, understand user requirements, and provide useful advice. The test results show favourable user evaluations and indicate how well the AI-based chatbot performs in providing healthcare services.Öğe Artificial Intelligence-based Health Data(CRC Press, 2024) Akarsu, Kamil; Hiziroglu, Ourania Areta; Er, OrhanThis book chapter provides a comprehensive examination of the importance of health data, the use of health data with artificial intelligence, and synthetic data generation. First, it focuses on the importance of health data, privacy and security, and the combined use of artificial intelligence with health data. Secondly, it focuses on the generation and purpose of synthetic data. Different methods for generating synthetic data, such as parametric modelling, Generative Adversarial Networks (GAN), and Variational Autoencoders (VAE), are discussed, and examples of applications and uses of synthetic data are provided. The benefits and challenges of synthetic data generation are also discussed. The chapter then discusses topics related to the application of artificial intelligence to health data, examining disease diagnosis and treatment recommendations, as well as applications of artificial intelligence in the field of personalized medicine. It then examines privacy and security legislation, ethical principles for the use and sharing of synthetic data, and different approaches to applications in the private and public sectors. Finally, it looks to the future, providing information on emerging technologies and applications of artificial intelligence-based health data analysis and management, and the future of anonymization and synthetic data generation techniques. This section provides a comprehensive view of the future potential and role of health data and artificial intelligence. © 2025 Mustafa Berktas, Abdulkadir Hiziroglu, Ahmet Emin Erbaycu, Orhan Er and Sezer Bozkus Kahyaoglu.Öğe Bölgesel Tabanlı Evrişimli Sinir Ağı ile Araç Plaka Tanıma(Düzce Üniversitesi, 2023) Çay, Talip; Ölmez, Emre; Er, OrhanBu çalışmada, Bölgesel Tabanlı Evrişimli Sinir Ağları (R-CNN) ile araç plaka lokasyonu belirleme ve belirlenen lokasyon içerisinden plaka okuma işlemi gerçekleştirilmiştir. İki aşamadan oluşan çalışmanın ilk aşamasında giriş görüntüleri üzerinden plaka lokasyonları R-CNN ile belirlenirken ikinci aşamada geleneksel görüntü işleme teknikleri ile belirlenen lokasyonlardan plaka okuma işlemi gerçekleştirilmektedir. Çalışmada tasarlanan R-CNN eğitiminde veri setinde bulunan 550 adet görüntüden 450 adedi eğitimde ve 100 adedi test işleminde kullanılmıştır. R-CNN ile plaka lokasyonu bulma işleminde test seti üzerinde %95 başarı oranına ulaşılırken doğru olarak belirlenen lokasyonlardan plaka okuma işleminde %97 başarı oranına ulaşılmıştır.Öğe Classification of Bovine Cumulus-Oocyte Complexes with Convolutional Neural Networks(2023) Çavuşoğlu, Türker; Gökhan, Aylin; Şirin, Cansın; Tomruk, Canberk; Kılıç, Kubilay Doğan; Ölmez, Emre; Er, OrhanAim: Determining oocyte quality is crucial for successful fertilization and embryonic development, and there is a serious correlation between live birth rates and oocyte quality. Parameters such as the regular/irregular formation of the cumulus cell layer around the oocyte, the number of cumulus cell layers and the homogeneity of the appearance of the ooplasm are used to determine the quality of the oocytes to be used in in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) methods. Material and Methods: In this study, classification processes have been carried out using convolutional neural networks (CNN), a deep learning method, on the images of the cumulus-oocyte complex selected based on the theoretical knowledge and professional experience of embryologists. A convolutional neural network with a depth of 4 is used. In each depth level, one convolution, one ReLU and one max-pooling layer are included. The designed network architecture is trained using the Adam optimization algorithm. The cumulus-oocyte complexes (n=400) used in the study were obtained by using the oocyte aspiration method from the ovaries of the bovine slaughtered at the slaughterhouse. Results: The CNN-based classification model developed in this study showed promising results in classifying three-class image data in terms of cumulus-oocyte complex classification. The classification model achieved high accuracy, precision, and sensitivity values on the test dataset. Conclusion: Continuous research and optimization of the model can further improve its performance and benefit the field of cumulus-oocyte complexes classification and oocyte quality assessment.Öğe Classification of Breast Cancer using Artificial Neural Network Algorithms(2021) Ölmez, Emre; Areta, Ourania; Er, OrhanBreast cancer is a malignant tumor that has developed from cells of the breast. Breast cancer is one of the most fatal diseases in the world and a relatively common cancer in Turkey. Breast cancer diagnosis has been approached by various machine learning techniques for many years. In this study, two different probabilistic neural network (PNN) structures were used for breast cancer’s diagnosis. The PNN results were compared with the results of the multilayer, learning vector quantization neural networks and the results of the previous reported studies focusing on breast cancer’s diagnosis and using the same dataset. It was observed that the PNN is the best classification accuracy with 98.10% accuracy obtained via 3-fold cross validation. The present paper describes how this technique can be applied to the breast tissue classification and the breast cancer detection for medical devices. The purpose of this study is the classification of the variability of impedivity observed in normal and pathological breast tissue.Öğe A CNN-Based Novel Approach for Classification of Sacral Hiatus with GAN- Powered Tabular Data Set(Kaunas Univ Technology, 2023) Kilic, Ferhat; Korkmaz, Murat; Er, Orhan; Altin, Cemilepidural anaesthesia is usually the most well-known technique in obstetrics to deal with chronic back pain. Due to variations in the shape and size of the sacral hiatus (SH), its classification is a crucial and challenging task. Clinically, it is required in trauma, where surgeons must make fast and correct selections. Past studies have focused on morphometric and statistical analysis to classify it. Therefore, it is vital to automatically and accurately classify SH types through deep learning methods. To this end, we proposed the Multi-Task Process (MTP), a novel classification approach to classify the SH MTP that initially uses a small medical tabular data set obtained by manual feature extraction on computed tomography scans of the sacrums. Second, it augments the data set synthetically through a Generative Adversarial Network (GAN). In addition, it adapts a two-dimensional (2D) embedding algorithm to convert tabular features into images. Finally, it feeds images into Convolutional Neural Networks (CNNs). The application of MTP to six CNN models achieved remarkable classification success rates of approximately 90 % to 93 %. The proposed MTP approach eliminates the small medical tabular data problem that results in bone classification on deep models.Öğe A comparative study on COVID-19 prediction using deep learning and machine learning algorithms: a case study on performance analysis(2022) Arslan, Hilal; Er, OrhanCOVID-19 disease has been the most important disease recently and has affected serious number of people in the world. There is not proven treatment method yet and early diagnosis of COVID-19 is crucial to prevent spread of the disease. Laboratory data can be easily accessed in about 15 minutes, and cheaper than the cost of other COVID-19 detection methods such as CT imaging and RT-PCR test. In this study, we perform a comparative study for COVID-19 prediction using machine learning and deep learning algorithms from laboratory findings. For this purpose, nine different machine learning algorithms including different structures as well as deep neural network classifier are evaluated and compared. Experimental results conduct that cosine k-nearest neighbor classifier achieves better accuracy with 89% among other machine learning algorithms. Furthermore, deep neural network classifier achieves an accuracy of 90.3% when one hidden layer including 60 neurons is used to detect COVID-19 disease from laboratory findings data.Öğe A comparative study on prediction of survival event of heart failure patients using machine learning algorithms(Springer London Ltd, 2022) Karakuş, Mücella Özbay; Er, OrhanCardiovascular diseases cause approximately 17 million deaths each year and 31% of deaths worldwide. These diseases generally occur as myocardial infarction and heart failure. The survival status, which we used as a target in our classification study, indicates that the patient died or survived before the end of the follow-up period, which is a mean of 130 days. Various machine learning classifiers have been preferred to both predict survival of patients and rank the characteristics corresponding to the most important risk factors. For this purpose, the data set that is occurred totally 299 samples is traditionally divided into 70% for training and 30% for test cluster to be used in machine learning algorithms, with have been analyzed with many methods such as Artificial Neural Networks, Fine Gaussian SVM, Fine KNN, Weighted KNN, Subspace KNN, Boosted Trees, and Bagged Trees. As a result, according to the data obtained, it has been seen that there are algorithms that can predict heart failure diagnosis with full accuracy (100%). Thus, it was concluded that it is appropriate to use machine learning algorithms to predict whether a heart failure patient will survive. This study has the potential to be used as a new supportive tool for doctors when predicting whether a heart failure patient will survive.Öğe How can Digital Robots Help Creating a Smart Campus?(Machine Intelligence Research (MIR) Labs, 2023) Akyol, Sakine; Dogan, Onur; Er, OrhanThe development of technology has created the need to increase the quality of life of individuals all over the world. This need for development has initiated the transformation of the ’smart campus’ in university campuses called small cities. A transformation that started with digitalization has led universities to use digital technologies in their business processes and education periods within the scope of smart campuses. Robotic Process Automation (RPA), one of the digital transformation technologies, automates repetitive and labor-intensive business processes in these digitalization processes. The application of RPA in four different processes at Izmir Bakýrçay University was examined to show how RPA can help creating a smart campus. The university saved up to 92.59% of cost and 98.25% of time from these four processes. It has been observed that the studies carried out within the scope of smart campuses will continue to increase because some routine processes still need to be automated. © (2023), (MIR Labs, www.mirlabs.net/ijcisim/index.html). All Rights Reserved.Öğe Machine learning approaches in the interpretation of endobronchial ultrasound images: a comparative analysis(Springer, 2023) Koseoglu, Fatos Dilan; Alici, Ibrahim Onur; Er, OrhanBackgroundThis study explores the application of machine learning (ML) in analyzing endobronchial ultrasound (EBUS) images for the detection of lymph node (LN) malignancy, aiming to augment diagnostic accuracy and efficiency. We investigated whether ML could outperform conventional classification systems in identifying malignant involvement of LNs, based on eight established sonographic features.MethodsRetrospective data from two tertiary care hospital bronchoscopy units were utilized, encompassing healthcare reports of patients who had undergone EBUS between January 2017 and March 2023. The ML model was trained and tested using MATLAB, with 80% of the data allocated for training/validation, and 20% for testing. Performance was evaluated based on validation and testing accuracy, and receiver operating characteristic curves with comparing trained models and existing classification rules.ResultsThe study analyzed 992 LNs, with 42.3% malignancy prevalence. Malignant LNs showed characteristic features such as larger size and distinct margins. The fine tuned models achieved testing accuracies of 95.9% and 96.4% for fine Gaussian SVM and KNN, respectively. Corresponding AUROC's were 0.955 and 0.963, outperforming other similar studies and conventional analyses.ConclusionFine tuned ML applications like SVM and KNN, can significantly enhance the analysis of EBUS images, improving diagnostic accuracy.Öğe The Impact of Artificial Intelligence on Healthcare Industry: Volume 1: Non-Clinical Applications(CRC Press, 2024) Berktas, Mustafa; Hiziroglu, Abdulkadir; Erbaycu, Ahmet Emin; Er, Orhan; Kahyaoglu, Sezer BozkusHealthcare and medical science are inherently dependent on technological advances and innovations for improved care. In recent times we have witnessed a new drive in implementing these advances and innovations through the use of Artificial Intelligence, in both clinical and non-clinical areas. The set of 2 volumes aims to make available the latest research and applications to all, and to present the current state of clinical and non-clinical applications in the health sector and areas open to development, as well as to provide recommendations to policymakers. This volume covers non-clinical applications. The chapters covered in this book have been written by professionals who are experts in the healthcare sector and have academic experience. © 2025 Mustafa Berktas, Abdulkadir Hiziroglu, Ahmet Emin Erbaycu, Orhan Er and Sezer Bozkus Kahyaoglu.Öğe The Impact of Artificial Intelligence on the Health Industry: General Framework on Non-clinical Applications(CRC Press, 2024) Berktas, Mustafa; Erbaycu, Ahmet Emin; Hiziroglu, Kadir; Er, Orhan; Kahyaoglu, Sezer BozkusThis chapter provides the general framework for a basic introduction to non-clinical applications of artificial intelligence (AI) in the healthcare industry. All activities related to patient care that are not observable in the disease treatment process can be referred to as non-clinical healthcare activities. The healthcare industry appears as a top priority in almost every subject in both advanced and emerging countries around the world. Therefore, this book is planned with the aim of closely examining and contributing to the dynamics of the healthcare sector, which has such a strategic importance and social impact. In this respect, Volume 1 covers non-clinical applications of AI and, as a next step, Volume 2 will provide information on the impact of AI on clinical implications. In this context, a wide range of topics will be described, from health big data and the characteristics of the health big data production process to key processes related to health service delivery, quality and accreditation. While the health industry is large in scale, it also has a very complex structure in terms of its operations, services and technologies. For this reason, it is one of the sectors expected to internalize and exploit technological advances most quickly. AI, which is contributing to revolutionary innovations in technological development, is spreading widely through intelligent tools with different algorithms based on machine learning and deep learning techniques. While the applications of artificial intelligence have benefits, they also pose challenges. The topics discussed in this book attempt to open the discussion on all aspects of AI with a balanced approach based on the literature. It provides value-added recommendations for public health management that should be considered in the development of health policies needed to improve service standards and quality, especially in the healthcare industry and the healthcare supply chain process. © 2025 Mustafa Berktas, Abdulkadir Hiziroglu, Ahmet Emin Erbaycu, Orhan Er and Sezer Bozkus Kahyaoglu.
