Enhancing random forest model for cancer detection: a multi-objective hyper parameter tuning strategy

Abstract

Early and accurate cancer detection is crucial for improving patient outcomes from this lethal disease, but traditional methods often lack sensitivity or specificity. Machine learning algorithms, particularly Random Forests, offer promising tools for analyzing medical data and achieving this goal. However, the performance of Random Forests is heavily dependent on the appropriate configuration of hyperparameters, requiring an optimal configuration for accurate prediction. This study proposes a novel multi-objective hyperparameter tuning strategy to enhance the effectiveness of Random Forests for cancer detection. The research starts off with single-goal hyperparameter tuning targeted on accuracy, observed by an evaluation of performance the usage of five-fold move-validation. The results show a crossvalidated training accuracy of 0.96 and a test accuracy of 0.97, effectively addressing the issue of overfitting. Subsequently, the methodology advances a state-of-the-art optimization algorithm, inclusive of a multi-objective algorithm or a particle swarm optimization, to explore the hyperparameter area efficiently. The proposed strategy aims to construct a Random Forest model that not only delivers accuracy but also maintains equilibrium across diverse performance aspects in cancer detection. To reap this, a multi-goal optimization algorithm is integrated with the hyperparameter tuning technique, enabling the exploration of numerous solutions across the Pareto front. This approach enhances the version's potential to parent diffused patterns indicative of cancerous conditions whilst minimizing false positives and fake negatives. The proposed multi-objective hyperparameter tuning approach for Random Forest models is a significant development in the field of cancer detection. In the end, it uses machine learning to improve healthcare outcomes by paving the way for more precise, understandable, and clinically relevant cancer diagnoses. It also highlights the significance of thorough hyperparameter optimization techniques in boosting model efficacy.