Data Mining Application on IVF Data For The Selection of Influential Parameters on Fertility
M. Durairaj1, R. Nandha Kumar2
1Dr. M. Durairaj, Asistant Professor, Department of Computer Science, Engineering and Technology, Bharathidasan University, Tiruchirappalli, India.
2Mr. R. Nandha Kumar, Research Scholar, Department of Computer Science, Engineering and Technology, Bharathidasan University, Tiruchirappalli, India.
Manuscript received on July 16, 2013. | Revised Manuscript received on August 20, 2013. | Manuscript published on August 30, 2013. | PP: 262-266 | Volume-2, Issue-6, August 2013. | Retrieval Number: F2068082613/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper illustrates the process applying data mining techniques for identifying influential tests for infertility couples to determine the success rate of IVF (In-vitro Fertilization) treatment. The data set used in the experiments contains information recorded during IVF treatment and relevant laboratory tests [1]. It has supportive information for the medical practitioner to identify which are tests have high impact factors in determining the success of infertility treatment. Data mining has so much of techniques that used to finding the data reduction, pre-processing and normalization [3].The reduced data set contain the set of parameters which have an influence on the results that can be used to predict and forecast [2]. The experiment is in a way of study related to the representativeness of the sample and irrelevant features. Out of around 250 million individuals estimated to be attempting parenthood at any given time, 13 to 19 million couples are likely to be infertile. So the couples prefer the IVF treatment compared with other methods of treatment. In India the board of medical council announced the duration of infertility. If a woman was not conceived after his marriage within 6 months they caused infertility. So they must start the initial fertility treatment. Most of them prefer the In-Vitro fertilization compare with other fertility treatments [9]. A survey of the fertility treatment 1 in 20 of all pregnancies conceived by the ivf treatment. But the patients suffer from the negative imagination and they don’t know the success level of the treatment. The prediction of the success rate of IVF treatment has a great economic importance for the couples who undergo treatment for baby [2]. The data set are preprocessed by the supervised filter and the attribute selection algorithm before subject to the prediction. It is very essential to properly analyze the data set and reduce or clean the unwanted data that increases the prediction accuracy [6]. The parameters with high impact factor can be selected by applying the proper reduct algorithm, which removes the parameters that has a lesser role in determining the success rate of particular patients and help the Gynecologists to recommend them for specific treatment of IVF, IUI or ICSI.
Keywords: Attribute selection algorithm, Data mining, IVF, Spermatological data, Supervised filter.