Kemal Akyol

Analysis of Demographic Characteristics Creating Coronary Artery Disease Susceptibility Using Random Forests Classifier

Abstract Cardiovascular system diseases are an important health problem. These diseases are very common also responsible for many deaths. With this study, it is aimed to analyze factors that cause Coronary Artery Disease using Random Forests Classifier. According to the analysis, we observed correct classification ratio and performance measure that creates susceptibility to Coronary Artery Disease for each factor. The performance measure results clearly show the impact of demographic characteristics on CAD. Additionally, this study shows that random forests algorithm can be used to the processing and classification of medical data such as CAD.

A Decision Support System for Early-Stage Diabetic Retinopathy Lesions

Retina is a network layer containing light-sensitive cells. Diseases that occur in this layer, which performs the eye-sight, threaten our eye-sight directly. Diabetic Retinopathy is one of the main complications of diabetes mellitus and it is the most significant factor contributing to blindness in the later stages of the disease. Therefore, early diagnosis is of great importance to prevent the progress of this disease. For this purpose, in this study, an application based on image processing techniques and machine learning, which provides decision support to specialist, was developed for the detection of hard exudates, cotton spots, hemorrhage and microaneurysm lesions which appear in the early stages of the disease. The meaningful information was extracted from a set of samples obtained from the DIARETDB1 dataset during the system modeling process. In this process, Gabor and Discrete Fourier Transform attributes were utilized and dimension reduction was performed by using Spectral Regression Discriminant Analysis algorithm. Then, Random Forest and Logistic Regression and classifier algorithms’ performances were evaluated on each attribute dataset. Experimental results were obtained using the retinal fundus images provided from both DIARETDB1 dataset and the department of Ophthalmology, Ataturk Training and Research Hospital in Ankara.

The Medical Module For Disaster Information Systems

The aim of this study is to improve a medical module providing data flow between data center and prehospital disciplines like nearby hospitals, field hospital, temporary inhabiting areas such as tent city and vehicles for transportation by using the internet. This study is conducted by using Microsoft Access database and SQL query to inform database applications so that the real-time information flow between health care experts and health care team in the disaster area is provided. System has been created on Microsoft .Net platform using C# language. MMDIS can be used kind of a field like all the pre-hospital processes. Web application enables access to database. On the other hand, the professionals who use this application have different permissions to access this database. The professionals’ duties specify those permissions. Interfaces have been created to access the database that enables data entry, data query, data storage, delete data etc. The users’ access to the web application is provided by www.afmedinfo.com. The real-users tests are organized to evaluate this applications achievements. These tests include the experiences of 13 users who are volunteers. The body language, hands and eyes movements of the users are being recorded with a camera. It will be measured how much and how long these duties will have been achieved by the end of April. The results obtained from these tests will be used for the reengineering of web applications. After that, these tests will be reapplied and obtained results will give the last form of this web application. Finally, new awareness that how disaster-based information systems are used in the field of health is improved with this study. This awareness provides not only the disaster information system which is an early warning system but also data flow which manages the process about victims in the disaster area. In addition to this, subjects and the differences of the health care practices of disaster information systems are uncovered. Keywords—Disaster Information System, Medical Module, Disaster, Medical Informatics