The Study of the Breast Cancer Prediction

Abstract

In recent years with the rapid advances in science and technology people have paid more attentions to self-health conditions by using health examination The health examination can avoid people missing the best time of disease diagnosis and treatment The medical records of patients and mammogram diagnosis are contributory factors of breast cancer Instead of using medical records or mammogram apart the proposed method combines features automatically extracted from mammograms and medical records of patients to build a breast cancer prediction model In preprocessing step of imaging data the proposed method uses fast and adaptive bidimensional empirical mode decomposition (FABEMD) to segment the mammograms for glandular tissue After integrating imaging data and clinical data the proposed method uses search constraints to select significant features The proposed approach solves the problem of the traditional decision tree which has complicated branches not only saves time but also effectively improves the accuracy of prediction model of breast cancer Our method was applied to real dataset which consists of 579 patients and the results show that the proposed method attains high accuracy of 98%

Date of Award	2014 Aug 15
Original language	English
Supervisor	Shu-Mei Guo (Supervisor)