A MapReduce-based Adjoint method for preventing brain disease

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In this paper, we present a statistical model performed on the basis of a patient dataset. This model predicts efficiently the brain disease risk. Multiple regression was used to build the statistical model. The least squares estimation problem usually used to estimate the parameters of regression model is solved via parallelized algebraic Adjoint method. As the parallelized algebraic Adjoint method is not the only Mapreduce-based method used to solve the least square problem, experimentations were carried out to classify the Adjoint method amongst the other methods. The calculated job completion time shows the competitive trait of the Mapreduce-based Adjoint method.

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