Purpose: This study investigates the potential of machine learning (ML) algorithms in improving sepsis diagnosis and prediction, focusing on their relevance in healthcare decision-making. The primary objective is to contribute to healthcare decision-making by evaluating the performance of various supervised and unsupervised models.
Materials and methods: Through an extensive literature review, optimal ML models used in sepsis research were identified. Diverse datasets from relevant sources were employed, and rigorous evaluation metrics, including accuracy, specificity, and sensitivity, were applied. Innovative techniques were introduced, such as a Stacked Blended Ensemble Model and Skopt Optimization with Blended Ensemble, incorporating Bayesian optimization for hyperparameter tuning.
Results: ML algorithms demonstrate efficacy in sepsis diagnosis, presenting an improved balance between specificity and sensitivity, critical for effective clinical decision-making. Classifier ensemble models show enhanced accuracy and efficiency, with novel optimization techniques contributing to improved adaptability.
Conclusion: The study emphasizes the potential benefits of ML algorithms in sepsis management, advocating for ongoing research to optimize performance and ensure ethical utilization in healthcare decision-making. Ethical considerations, interpretability, and transparency are crucial factors in implementing these algorithms in clinical practice.
Keywords: Artificial intelligence; Ensemble learning methods; Healthcare decision making; Reinforcement learning; Sepsis; Supervised learning; Unsupervised learning.
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