Medical equipment maintenance decision model with data mining

Information technology and the development of data base systems in order to reach the large volume of data and the need to convert that data into meaningful information has emerged. Data mining is widely used for this purpose in recent years and is striking techniques. Everything we do in our daily lives as a result of transactions are very large amounts of data derived from large amounts of data but very little meaningful information. Large amounts of data to obtain necessary information, new techniques and softwares are being devoloped. At this point, data mining, through these piles of data that provide meaningful and useful information extraction is an interdisciplinary approach. While doing this, especially in statistics, database technology, machine learning, artificial intelligence and visualization are benefiting from. Medical devices is one of the indispensable component of the health sector with pharmaceuticals sector. With the continuous devolopment of technology in the world, medical devices are constantly subject to change. Today, that has become an industry with high added value, and constitutes an important input for the healthcare industry experience related to the medical devices sector is a sector different from other sectors due to be addressed. Due to malfunction of medical devices that perform vital activities for a few days to work even in the diagnosis and treatment of patients as well as property damage can lead to delays. Our study, aimed to determine the most suitable maintenance intervals for medical devices. For analyzing the data, Apriori Algorithm was used. A-priori algorithm is only applied to the discrete variable so continuous variables are discretized too. Key Words: Decision Support, Data Mining, Medical Device