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Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.

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It is seen that distance education method which is a rationalist, contemporary and innovative education system, is started to be used widely with transportation of education and training activities to internet area fast, in recent years. Also, distance education contributes to human's lifelong learning through giving education opportunities to working people whose financial situation and time are limited. With the development of information technologies in the world and Turkey, distance education methods and techniques also developed. Also the more the number of students taking part in distance education rises, the more the number of institutions giving distance education rises. Needs for preparing contents and managing these contents for lessons given by distance education method appeared with this raise. On this study, a platform has been developed for lecturers to create lesson contents and the efficiency of developed implementation has been analyzed statistically. It is aimed on distance education to design an efficient content development system through thinking the roles such as lecturers, managers and students.