Proposed Model to Detect Uterine Fibroid By using Data Mining Techniques

Abstract

Data mining is a multi-step process that uses a large amount of data. All of the systems components are interrelated. To get started, we need to get some raw data. The data is first pre-processed and then converted into formats that may be used by the various mining tools. The missing values can be supplied either during pre-processing or via a separate tool, as will be discussed later in this WEKA programme section. Diverse data mining techniques uncover patterns in the cleansed material that are common to both. Commonalities in data can be discovered through the identification of patterns and rules in the data. This information can be used to classify the female uterine fibroid with precision.