The method of identifying the disease with person’s symptoms and signs is medical diagnosis. Brain tumour is the stimulating disorder that has to be identified at early stage for treatment. Many classification techniques have been introduced for performing brain tumour identification. However, the brain tumour identification accuracy level was not enhanced and time consumption was not lessened. In order to address these problems, Projection Pursuit Feature Selective Bivariate Multilayer Perceptred Classification (PPFSBMPC) Method is introduced. PPFSBMPC Method comprises two processes, namely feature selection and classification for brain tumour identification. To select the relevant features from the input database, Projection Pursuit Feature Selection process is carried out in PPFSBMPC Method. After performing the feature selection, Bivariate Multilayer Perceptred Classification process is accomplished for brain tumor identification. In addition, the classification process comprised multiple layers to categorize the input data as normal data or tumour diseased data. By this way, PPFSBMPC Method increases the brain tumor identification performance with higher accuracy and lesser time consumption. Experimental evaluation of PPFSBMPC Method is carried out with Epileptic Seizure Recognition Dataset on factors such as brain tumour identification accuracy, execution time, and error rate with respect to number of patient data. The experimental result demonstrates that the PPFSBMPC Method enhances the brain tumour identification accuracy and reduces the execution time when compared to state-of-the-art-works.

Medical diagnosis, brain tumour, classification, feature selection, classification process, identification, seizure

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