Semantic Risk Analysis Model Cancer Data Prediction
In this paper breast cancer risk assessment model can assess whether a user is at a high risk of developing breast cancer disease or not and confirm a breast cancer high-risk group. Because the etiology of breast cancer disease is different in different country and region, the existing risk assessment model is only adaptive to certain countries and regions. And the parameters of these models are fixed, so these models have poor generality. Aiming at these problems, the paper puts forward a new breast cancer risk assessment model named as Shrink. Using the idea of social network, Shrink constructs a medical social network to show the similarity among user, and uses group division algorithm to divide the network into breast cancer high-risk group and low-risk group. The parameters of this model can be set according to the needs of the breast census, and these parameters can be directly acquired through questionnaire, therefore Shrink has good generality. Moreover, under the uncertain classification standard, Shrink adopts a new classification method to discover breast cancer high-risk group.
In addition Shrink model solves the poor generality of the existing model. The factors used by the SVM model are acquired through questionnaire, and the model doesn’t depend on certain factors. Every country can set factors according to the need of the breast census. Under uncertain classification standards, Shrink uses group division algorithm to discover breast cancer high-risk group. Also, through experiment analysis, the SVM model itself has good judgment function, and the model is better than KNN model. Therefore, improve results the prevention and control of breast cancer.
Author Name: S.B. Jayabharathi, S. Manjula, E. Tamil Selvan, P. Vengatesh and V. Senthil Kumar
Author Email: firstname.lastname@example.org
Phone Number: -
College: K.S. Rangasamy College of Technology, Tiruchengode
Keywords: Breast Cancer Detection, Group Division Algorithm, SVM Model, Social Network, KNN Classification Model.