Key result
A novel machine learning approach integrating Particle Swarm Optimization with Naive Bayes significantly enhanced the accuracy and reliability of predicting 2-year mortality in patients with Chagas disease.
Why the study?
Handling imbalanced data remains an unresolved issue in healthcare machine learning, which is critical for the efficiency of classification algorithms used in patient health risk management, monitoring, diagnosis, and prognosis.
Applying data balancing techniques significantly improves the performance of machine learning models in predicting rare medical events such as stroke.
May enhance ML classification for imbalanced healthcare data; leaves open optimal technique selection before clinical adoption.
Machine learning has increasingly gained prominence in the healthcare sector due to its ability to address various challenges. However, a significant issue remains unresolved in this field: the handling of imbalanced data. This process is crucial for ensuring the efficiency of algorithms that utilize classification techniques, which are commonly applied in risk management, monitoring, diagnosis, and prognosis of patient health. This study conducts a comparative analysis of techniques for handling imbalanced data and evaluates their effectiveness in combination with a set of classification algorithms, specifically focusing on stroke prediction. Additionally, a new approach based on Particle Swarm Optimization (PSO) and Naive Bayes was proposed. This approach was applied to the real problem of Chagas disease. The application of these techniques aims to improve the quality of life for individuals, reduce healthcare costs, and allocate available resources more efficiently, making it a preventive action.
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Oliveira et al. (2025) studied Chagas disease (n=551). Particle Swarm Optimization (PSO) combined with Naive Bayes vs. Naive Bayes alone, Multilayer Perceptron, and Genetic Algorithm combined with Naive Bayes was evaluated on Prediction of patient mortality (F1-Score, Accuracy, AUC-ROC). A novel machine learning approach integrating Particle Swarm Optimization with Naive Bayes significantly enhanced the accuracy and reliability of predicting 2-year mortality in patients with Chagas disease.
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