Data mining techniques used for uterus fibroid diagnosis and prognosis

The availability of massive amounts of medical data leads to the requirement for powerful data analysis tools to extract useful knowledge. Researchers have long been committed applying statistical and data processing tools to spice up data analysis on large data sets. Health problem identification is one in every of the applications where data mining tools are proving flourishing results. Uterus Fibroid diagnosis and Prognosis square measure two medical applications cause a good challenge to the researchers. The employment of machine learning and method techniques has revolutionized the entire process of Fibroid diagnosis and Prognosis. The diagnosis of fibroid present within the different parts of the female internal reproductive organ distinguishes it´s eliminated or detain the female internal reproductive organ. Fibroid Prognosis predicts once Fibroid is probably going to recur in patients that have had their cancers excised. Thus, these two issues are mainly within the scope of the classification issues. This study paper summarizes numerous data mining techniques, review and technical articles on Fibroid diagnosis and prognosis. During this paper we tend to present an outline of the present research being carried out using the data mining techniques to reinforce the Fibroid diagnosis and prognosis.