Integrated Intelligent Research (IIR) International Journal of Data Mining Techniques and Applications Volume: 04 Issue: 02 December 2015 Page No.63-66 ISSN: 2278-2419 63 A Pioneering Cervical Cancer Prediction Prototype in Medical Data Mining using Clustering Pattern R.Vidya 1 ,G.M.Nasira 2 1 Research scholar of M.S.University,Tirunelveli, Assistant Professor /Department of Computer Science St.Joseph’s college, Cuddalore, India 2 Assistant Professor/Department of Computer Science, ChikkannaGovernment College for Women, Tirupur, India Email: vidya.sjc@gmail.com, nasiragm99@yahoo.com Abstract—Let us not make the cure of the disease more unbearable than the disease itself this quote is the most durable and inspirational line of medicine field. Data mining is said to be an umbrella term which refers to the progression of finding out the patterns in data. This can be even succeeded typically with an assistance of authoritative algorithm to automate search (as a part). This paper reveals out, how the C2P (Cervical Cancer Prediction) model is approached by a data mining algorithm for prediction. The prediction of C2 (Cervical Cancer) has been a challenging problem in research field. In the Data mining applications, we are utilizing RFT (Random Forest Tree) algorithm to do the prediction. To the best of our knowledge, we use popular clustering K-means technique to achieve more accuracy. Keywords— Cervical Cancer, Random Forest Tree, K-means learning, Data mining, Clustering K-means. I. INTRODUCTION Cancer of the cervix is one of the most prevalent forms of cancer worldwide, the major burden of the disease being felt in developing countries like India. Cervical carcinoma still continues to be the most common cancer among women and accounts for the maximum cancer deaths each year. Persistent infections with High-Risk (HR) Human Papilloma viruses, such as HPV 16,18,31,33 and 45 have been identified as a major development of the distance [8]. This model interpretability and prediction accuracy provided by Random Forest is very unique among popular machine learning methods. Accurate predictions and better generalizations are achieved due to utilization of ensemble strategies and random sampling.Random Forest three main features that gained focus are: (i) Accuratepredictions results for a variety of applications. (ii) Through model training, the importance of each feature can be measured (iii) Trained model can measure the pair-wise proximity between the samples. The research of our paper is as followed by cervical cancer and diseases and then proceeded by medical data mining and RFT where we deal with K-Mean algorithm in the next phase. Followed by this we have given out our C 2 P Model along with how to prevent cancer. Later the paper concludes by Result and conclusion. Characterized by abnormal bleeding, pelvic pain and unusual heavy discharge, the disease develops in the tissues of the cervix, a part connecting the upper body of the uterus to the vagina. II. CERVICAL CANCER DISEASE AND ITS SYMPTOMS In worldwide C 2 is the third most common cancer in women and seventh overall. Majority of this global burden is felt in low and middle income developing countries (WHO, 2010) and in low socio-economic groups within countries [1].It comprises of endocervix or the upper part which is close to the uterus and covered by glandular cells; and the ecocervix, the lower part which is close to the vagina and covered by Seamus cells. The two regions of the cervix meet at the ―transformation zone‖. It is this region where most cervical cancer begins to develop [6]. Figure 1. Cervix Cancer with its Cells (Developing Stage) A. Signs & Symptoms of Cervical Cancer Early cervical cancer has no symptoms and it cannot be identified in a clear way. Symptoms do not begin until the pre- cancer grows to an aggressive stage and starts to spread to nearby tissue. When this happens the most common symptoms are: Figure 2. Cancer Cells in Progressive Stage