Analysing Big Data to Build Knowledge Based System for Early Detection of Ovarian Cancer

Big data analysis plays a crucial role in the health care for early diagnosis of fatal disease. The data mining techniques are widely used for data analysis problem to discover valuable knowledge from a large amount of data. This paper uses the data mining methods such as feature selection and classification to provide a predictive model for ovarian cancer detection. A huge amount of dataset is gathered to build knowledge based system. Rough set theory is utilized to find the data reliance and reduce the feature set contained in the data set. The Hybrid Particle Genetic Swarm Optimization (PGSO) is used to optimize the selected features to efficiently classify the ovarian cancer, either normal or early or different stages of ovarian cancer. Multi class SVM is adopted as the classifier to classify normal or different stages of ovarian cancer using the optimized feature set. The experiment is done on different ovarian cancer dataset and the proposed system has obtained better results for all datasets.

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