A classified medical infertility dataset using high utility item set mining

Abstract

The most modern technological innovations led towards generating lots of data, which is either redundant or of imperative use. To mine the meaningful information from this huge repository, Data mining techniques will be of vital importance. This article aims at mining the useful patterns from this enormous repository and presents some possible solutions while treating the patients suffering with various problems of infertility. A Classified High utility item set mining with Naïve Bayes classification (CHUIM-NB) is proposed for classifying the data, which will be of productive usage to the Medical Practitioners during the treatment of the patients. The proposed model has three stages: the stage1 aims at generating the training data, the second stage aims at proposing a two phase algorithm for producing high utility item set and also the rules for association mining (CHUIM) and in the third stage, the Naives classification model (CHUIM-NB) is considered for the effective diagnoisis/ treatment.