Predicting healthcare utilizing new machine learning techniques and metaheuristic algorithms

Area of Science:

• Computational biology
• Medical informatics
• Machine learning applications in healthcare

Background:

• Accurate prediction of disease outcomes is crucial for effective patient management and healthcare planning.
• Current diagnostic systems face challenges in achieving high predictive accuracy.
• Integrating advanced computational techniques can potentially overcome these limitations.

Purpose of the Study:

• To develop and evaluate a novel predictive system for disease outcome diagnosis.
• To enhance diagnostic accuracy by merging ensemble learning models with metaheuristic optimization techniques.
• To investigate the impact of bio-inspired optimization on machine learning model performance in healthcare.

Main Methods:

• Implementation of a predictive system combining Random Forest Classifier (RFC) and Gradient Boosting Classifier (GBC).
• Integration of metaheuristic optimization algorithms, namely Artificial Rabbit Optimization (ARO) and Dragonfly Optimization (DOA), to tune hyperparameters.
• Evaluation of the proposed models, including RFAR (RFC + ARO), GBAR (GBC + ARO), and RFDO (RFC + DOA), on a synthetic healthcare dataset.

Main Results:

• The RFAR model demonstrated superior performance, achieving the highest accuracy of 0.989.
• GBAR and RFDO models also showed improved predictive performance compared to standard methods.
• Metaheuristic optimization significantly enhanced the hyperparameter tuning process for ensemble learning models.

Conclusions:

• The combination of ensemble learning and metaheuristic optimization offers a significant improvement in predictive accuracy for disease outcomes.
• The developed models show promise for supporting automated diagnostic systems and personalized healthcare planning.
• This approach provides a robust framework for advancing precision medicine through enhanced predictive analytics.