Cancer Detection Methodology using Adaptive Neuro Fuzzy Inference System and Support Vector Machine Classification

Data mining is a process of extracting hidden knowledge from large volumes of data. It is used intensively in the field of medicine to predict diseases such as heart diseases, lung cancer, breast cancer and more. Medical data mining has great potential for exploring the hidden patterns in the data sets of the medical domain; such patterns are utilized for medical diagnosis. Medical images play an important role in assisting diagnosis and treatment of healthcare management systems. The advancements and large volumes of medical image data become major challenges. In this paper, a novel method to enhance the performance of classifiers Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) through feature selection is proposed. The feature selection methods Genetic Algorithm (GA) and Rough Set (RS) are used to select the features. This research work mainly focuses on selecting the prominent features to improve the accuracy of the classification algorithms. Experimentation has been made on various medical images. The performance of the classification algorithms is estimated in terms of increase in accuracy after feature selection.