A Fast Parallel Classification Model for the Diagnosis of Cancer

Background: In this era of voluminous data, there is a need to process data speedily in less time. Moreover, it is also essential to diminish the dimensionality of data as well as to apply parallel computations for classification. SVM is a prominent classification tool and is currently one of the most popular state-of-the-art models for solving various classification problems that makes use of parallel computations to speed up its processing. Objective: To develop a fast, promising classification system using optimized SVM classifier with hybridized dimensionality reduction for the diagnosis of cancer disease. Methods: The proposed approach comprises of two stages – the first stage presents a hybrid approach to reduce the dimensionality of cancer datasets, and the second stage presents an efficient classification method to optimize the SVM parameters and improve its accuracy. To lessen the execution time, the proposed approach uses GPUs to concurrently run different processes on machine workers. Results: The proposed method with the combination of dimensionality reduction & parallel classification using optimized SVM classifier is found to give excellent results based on ‘Classification Accuracy’, ‘Selected Features’ and ‘Execution Time’. Conclusion: The experimental findings with benchmark datasets indicate that the proposed diagnostic model yields significant improvement in terms of execution time when compared to the conventional approach. The proposed model can assist doctors and medical professionals in the quick selection of most significant risk factors for the diagnosis and prognosis of cancer diseases.