Limbless Medical Data Analyzing using CRISP Model a Case Study of UK Limbless Patients - Shams Alden, Zahraa; Hameed Mousa, Ayad (International Journal of Engineering and Technology, 2018)
Data mining, usually known as knowledge elicitation in the field of computer science databases, is the procedure to find out an important relationship, useful patterns in a huge amount of raw data. Besides, many sectors have adapted and used data mining in their applications such as healthcare and industry sector. In the healthcare sector, data mining can help in determining the probability of particular health cases in medical issues which the related variables pre-known as well as predicting future events. The availability of medical data for data mining usually exist in a raw data format, therefore, it needs for making ready and exploration to be willing to use. In the context of this paper, an analyzing of medical data was introduced to support prosthetics service centers to analyze find out the significant information from limbless medical cases, besides, in providing a comprehensive understanding of amputation and its types as well as the level of amputation. To ensure extract meaningful information from the intended data sets as well as to follow a systematic approach, the CRISP-DM model was adopted. The findings show the important and meaningful of the analyzing data using data mining modes.Â