Effectiveness of Data Mining - based Cancer Prediction system (DMBCPS)

Abstract

Cancer is one of the deadly diseases in the world today. Cancer is caused because of some genetic factors and/or environmental factors and/or today's modern lifestyle. Cancer has become the primary reason of death in developed countries. The most effective way to reduce cancer death is to detect it earlier. The earlier detection of cancer is not easier process but if it is detected, it is curable. Many works have been done in predicting cancer; different data mining approaches and algorithms were adopted by different people. Each work has some limitations such as lack of intelligent prediction, and inefficient in structure that motivated to take up this problem and to implement the Data mining based cancer prediction System (DMBCPS). We have proposed the cancer prediction system based on data mining. This system estimates the risk of the breast, skin, and lung cancers. This system is validated by comparing its predicted results with patient's prior medical information and it was analyzed by using weka system. The main aim of this model is to provide the earlier warning to the users, and it is also cost efficient to the user.

Prediction of Breast Cancer Using Big Data Analytics

“…Some of the well-known decision tree algorithms are C4.5, J48 and ID3 etc. The Decision tree is divided as regression tree and classification tree [13].…”

Novel Classification based approaches over Cancer Diseases

“…So, they recommended that users should try their data set on a set of classifiers and choose the best one. Priyanga and Prakasam [2] proposed a cancer prediction system based on data mining technology by examining a number of user-provided genetic and non-genetic factors. The system estimated the risk of the breast cancer in the earlier stage.…”

Empirical Analysis of Cervical and Breast Cancer Prediction Systems using Classification

“…There is sufficient literature written on cancer prediction systems. After reviewing and analyzing the existing studies [5][6][7][8][9][10][11], there shortcomings and limitations were observed. This paper focusses on the review of attributes used for the datasets of existing breast and cervical cancer prediction systems and proposes new attributes after identifying the limitation of the existing attributes.…”