Multi-layer perceptron classification method of medical data based on biogeography-based optimization algorithm with probability distributions

In the field of medical informatics, the accuracy of medical data classification plays a vital role. Multi-layer Perceptron (MLP), as one of the most widely used neural networks, has been widely used in the medical fields. In recent years, the Biogeography-based Optimization (BBO) algorithm has been proposed to train MLP, but the original algorithm often encounters local minimums, slow convergence, and sensitivity to initialized values during the optimization process. To this end, this paper adopted the different probability distributions to improve the BBO (PD-BBO) algorithm to train MLP so as to improve medical data classification accuracy. These distributions include Gamma distribution, Beta distribution, Gaussian distribution, Exponential distribution, Poisson distribution, Geometric distribution, Rayleigh distribution and Weber distribution Then these different probability distributions were embed into the migration process of the BBO algorithm to replace the random distribution and the migration probability was defined. Finally, simulation experiments were carried out, and the benchmark function was used to prove the effectiveness of the proposed algorithms. And then it was used to train a multi-layer perceptron, and five medical data sets were selected for classification. After that, the performance of the standard BBO algorithm and five typical meta-heuristic algorithms were compared. The results showed that the PD-BBO algorithms to train MLP was better than the BBO algorithm and the selected meta-heuristic algorithms, and the classification accuracy has been improved to a certain extent.