A genetic algorithm-based multi-objective optimization of an artificial neural network classifier for breast cancer diagnosis

The conventional technique for diagnosing the breast cancer disease relies on human experiences to identify the presence of certain pattern from the database. It is time-consuming and incurs unnecessary burden to radiologists. This work proposes a genetic algorithm-based multi-objective optimization of an Artificial Neural Network classifier, namely GA-MOO-NN, for the automatic breast cancer diagnosis. It performs a simultaneous search for the significant feature subsets and the optimum architecture of the network. The combination of ANN’s parameters with feature selection to be optimized by Genetic Algorithm is novel. The Pareto-optimality with new ranking approach is applied for simultaneous minimizations of two competing objectives: the number of network‘s connections and squared error percentage of the validation data. Result shows that the algorithm with the proposed combination of objectives has achieved the best and average, 98.85 and 98.10 % accuracy of classification, respectively, on breast cancer dataset which outperform most systems of other works found in the literature.