A Novel Approach for Women’s Infertility Detection Using Data Mining Techniques

Anamika Arora; Dr. Pradeep Chouksey

In today’s life infertility is the main problem going on new generation which also affect the population as well s daily life of couples. It is estimated that by survey by 2025 70% couples are affected. The medical sciences gathering information to how to stop rate of increasing infertility in women’s. Unfortunately the data are not mined to have hidden information for effective decision making due to lack of effect of analysis tools to discover hidden relationships and trend in data. this research paper provide a survey of current techniques of knowledge discovery in databases using data mining techniques which are very useful for medical practitioners to take effective decision. The objective of this research work is to predict more accurately the presence of infertility in women’s with reduced no of attributes. For predicting infertility the women’s goes from much diagnosis. And for predicting infertility around sixteen attributes are required for detecting infertility or not. in this research work sixteen attributes reduce to eight attributes .three classifiers like Naive bayes, J48 decision Tree and Bagging algorithm are used to predict the diagnosis of patients with the same accuracy as obtained before the reduction of number of attributes. In our work 10-fold cross validation method was used to measure the unbiased estimate of these prediction models.

Select Volume / Issues:

• Volume 8,Issue 2, Mar. 2017

Year:

2017

Type of Publication:

Article

Keywords:

Data Mining; Infertility Diagnosis; Naive Bayes; J48 Decision Tree; Bagging Algorithm

Journal:

IJECCE

Volume:

8

Number:

2

Pages:

129-133

Month:

March

ISSN:

2249-071X