Open AccessCervical Cancer Risk Factors: Classification and Mining Associations
Author(s) -
Arif- Ul- Islam,
Shamim Ripon,
Nuruddin Qaisar Bhuiyan
Publication year - 2020
Publication title -
aptikom journal on computer science and information technologies
Language(s) - English
Resource type - Journals
eISSN - 2528-2425
pISSN - 2528-2417
DOI - 10.34306/csit.v4i1.85
Subject(s) - cervical cancer , decision tree , cancer , random forest , cervix , association rule learning , stage (stratigraphy) , logistic regression , medicine , tree (set theory) , cancer stage , computer science , data mining , artificial intelligence , gynecology , machine learning , mathematics , paleontology , mathematical analysis , biology
Women of all over the world suffer from a common cancer, named Cervical cancer. Cervical cancer cellsgrow slowly at the cervix. This cancer can be avoided if it is recognized and handled in its first stage. Now it is a keychallenge for Medical experts to identify such cancer before it develops extremely. Nowadays, data mining modelsare popularly used to extract hidden patterns from huge medical dataset. This paper introduces data miningtechinques for classification and finding associations in order to detect Cervical cancer at early stage. Afterpreprocessing, the dataset was tested on Decision Tree, Random Forest, Logistic Model Tree and Artificial NeuralNetwork. These methods achieve considerable success in case of both K-fold cross validations and randomly splitdataset. Association rules has been established for detecting comparatively riskier factors which are moreresponsible for cancer development. The proposed methodology can help Medical experts to conduct their researchon Cervical cancer.