Evaluation of Predictive Ability of Some Data Mining and Statistical Techniques Using Breast Cancer Dataset


Authors : H.G. Dikko, Y. Musa, H.B. Kware.

Volume/Issue : Volume 2 - 2017, Issue 10 - October

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://goo.gl/VuWfx4

Thomson Reuters ResearcherID : https://goo.gl/3bkzwv

There is no single best algorithm since it highly depends on the data any one is working with. Nobody can tell what should use without knowing the data and even then it would be just a guess. This research work focuses on finding the right algorithm that works better on breast cancer data sets. The aim of this study is to perform a comparison experiment between statistical and data mining modeling techniques. These techniques are Data mining Decision Tree (C4.5), Neural Network (MLP), Support vector machine (SMO) and statistical Logistic Regression. The comparison will evaluate the performance of these prediction techniques in terms of measuring the overall prediction accuracy for each technique on the bases of two methods (cross validation and percentage split). Experimental comparison was performed by considering the breast cancer dataset and analyzing them using data mining open source WEKA tool. However, we found out that a C4.5 and MLP algorithm has a much better performance than the other two techniques.

Keywords : Breast Cancer Survivability, Multi-Layer Perception, Logistic Regression, Data Mining.

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