Development of a Multiprocessing Interface Genetic Algorithm for Optimising a Multilayer Perceptron for Disease Prediction

TL;DR

This paper presents a multiprocessing genetic algorithm framework that efficiently optimizes multilayer perceptrons for disease prediction, achieving high accuracy and significantly reducing tuning time across multiple datasets.

Contribution

It introduces a modified multiprocessing genetic algorithm (MIGA) that parallelizes hyperparameter tuning for MLPs, improving efficiency and accuracy over traditional methods.

Findings

Achieved up to 99.12% accuracy on breast cancer dataset.

Reduced tuning time by approximately 60% with MIGA.

Outperformed grid, random, and Bayesian optimization methods.

Abstract

This study introduces a framework that integrates nonlinear feature extraction, classification, and efficient optimization. First, kernel principal component analysis with a radial basis function kernel reduces dimensionality while preserving 95% of the variance. Second, a multilayer perceptron (MLP) learns to predict disease status. Finally, a modified multiprocessing genetic algorithm (MIGA) optimizes MLP hyperparameters in parallel over ten generations. We evaluated this approach on three datasets: the Wisconsin Diagnostic Breast Cancer dataset, the Parkinson's Telemonitoring dataset, and the chronic kidney disease dataset. The MLP tuned by the MIGA achieved the best accuracy of 99.12% for breast cancer, 94.87% for Parkinson's disease, and 100% for chronic kidney disease. These results outperform those of other methods, such as grid search, random search, and Bayesian optimization. Compared with a standard genetic algorithm, kernel PCA revealed nonlinear relationships that improved classification, and the MIGA's parallel fitness evaluations reduced the tuning time by approximately 60%. The genetic algorithm incurs high computational cost from sequential fitness evaluations, but our multiprocessing interface GA (MIGA) parallelizes this step, slashing the tuning time and steering the MLP toward the best accuracy score of 99.12%, 94.87%, and 100% for breast cancer, Parkinson's disease, and CKD, respectively.