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Cervical Cancer Diagnosis Using Data Mining Algorithm

Johnson Gurneey

A class of data mining techniques can be used to accurately diagnose cervical cancer, which has significant practical implications. In particular, the beneficial information present in a sizable amount of medical data may not only subtly advance medical technology but also, in the future, aid in the detection of cervical cancer. In order to collect and analyse picture information, this study enhances the data mining algorithm and integrates image recognition and data mining technologies. Additionally, this study fully exploits the image data to segment the cervical cancer cell image, choose the feature vector in accordance with the features of the cervical cancer cell, and create the classifier using the statistical classification approach. The test results demonstrate that this system’s automatic recognition and supplementary diagnosis effects are both good. As a result, it can be confirmed in clinical settings throughout the follow-up.