Comparative Study on Different Classification Techniques for Ovarian Cancer Detection

Diagnosing ovarian cancer is a medical challenge to clinical researchers. This study aims to develop a novel prototype of clinical management in diagnosis and management of patients with ovarian cancer. Various classification algorithms can be applied to cancer databases to devise methods that can predict cancer manifestation. Various methods, however, vary in terms of the level of accuracy, depending on the classification algorithm used. Identifying the most accurate classification algorithm is a challenging task, primarily due to limited data availability. In this paper, a comprehensive comparative analysis of nine different classification algorithms was conducted and their performances have been evaluated. The results indicate that all classifiers are relatively equal in accuracy, meaning that multiple classifying techniques can be used to support physicians in rendering more informed diagnostic decisions.

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