Differential evolution optimization algorithms and its application in machine learning based disease detection

Abstract

Machine learning (ML) in healthcare is crucial for establishing and enhancing performant automated disease detection systems that can be used in medical practice. This dissertation aims to improve ML classifiers for disease detection through three main objectives: optimising hyperparameters using Differential Evolution (DE), comparing the performance of DE-optimised classifiers (Random Forest, AdaBoost, Gradient Boosting) against traditional methods, and exploring DE's application in automating triage systems in healthcare. Research employed datasets from the UC Irvine Machine Learning Repository, focusing on heart disease and thyroid cancer due to their prevalence. Data analysis involved optimising classifiers with DE and assessing their performance. The study identified the DE-optimised Random Forest classifier as particularly effective, achieving 98.7% accuracy and a 97.2% F1-score in thyroid cancer recurrence, and 93.3% accuracy with a 90.9% F1-score for heart disease. These results underscore DE's potential in enhancing accuracy and efficiency of ML classifiers. The findings also suggest significant implications for DE in healthcare, especially in automated triage systems, indicating a transformative impact on predictive diagnostics. The dissertation concludes with recommendations for integrating DE-optimised ML classifiers in healthcare settings and suggestions for future research.