⚡ Quick Summary
This study presents a novel approach to predicting respiratory diseases, particularly COVID-19, using a genetic algorithm for hyperparameter optimization and an ensemble model that incorporates explainable AI. The proposed model demonstrated superior prediction accuracy compared to existing methods, highlighting the potential of machine learning in clinical diagnostics.
🔍 Key Details
- 📊 Dataset: Mexico clinical dataset of COVID-19
- 🧩 Features used: Various clinical parameters
- ⚙️ Technology: Genetic algorithm for hyperparameter optimization, binary grey wolf optimization for feature selection, stacking classifier for ensemble modeling
- 🏆 Performance: Adaboost algorithm outperformed other models in prediction accuracy
🔑 Key Takeaways
- 💡 Machine learning can significantly enhance the early diagnosis of respiratory diseases.
- 🔍 Hyperparameter optimization is crucial for improving model performance.
- 🤖 Explainable AI provides insights into feature importance, aiding clinical decision-making.
- 🏥 The ensemble model using stacking classifiers showed promising results in accuracy.
- 📈 Performance metrics such as accuracy, precision, recall, AUC, and F1-score were utilized for evaluation.
- 🌍 The study emphasizes the importance of data-driven approaches in healthcare.
- 🧠 Future research could explore further enhancements in model accuracy and applicability.
📚 Background
The rise of machine learning in healthcare has opened new avenues for disease diagnosis and management. Respiratory diseases, particularly those caused by viruses like COVID-19, pose significant challenges to healthcare systems worldwide. Early and accurate diagnosis is essential to mitigate the impact of such diseases, making the development of advanced predictive models a priority in medical research.
🗒️ Study
This study aimed to develop an improved model for predicting respiratory diseases by integrating hyperparameter optimization and feature selection techniques. The researchers utilized a publicly accessible dataset from Mexico, focusing on COVID-19 cases. By employing a genetic algorithm for hyperparameter tuning and a binary grey wolf optimization algorithm for feature selection, the study sought to enhance the predictive capabilities of machine learning models.
📈 Results
The findings revealed that the proposed model achieved superior prediction accuracy compared to existing models. Among the various hyperparameter-optimized algorithms tested, the Adaboost algorithm emerged as the most effective, outperforming its counterparts in key performance metrics. The study utilized a comprehensive set of evaluation metrics, including accuracy, precision, recall, AUC, and F1-score, to validate the model’s effectiveness.
🌍 Impact and Implications
The implications of this research are profound, as it demonstrates the potential of machine learning to revolutionize respiratory disease diagnosis. By leveraging advanced algorithms and explainable AI, healthcare professionals can make more informed decisions, ultimately improving patient outcomes. This study paves the way for further exploration of AI-driven solutions in clinical settings, potentially transforming how respiratory diseases are diagnosed and managed.
🔮 Conclusion
This study highlights the remarkable potential of machine learning in enhancing the prediction of respiratory diseases. The integration of hyperparameter optimization and explainable AI not only improves accuracy but also provides valuable insights into the factors influencing predictions. As research in this field continues to evolve, we can anticipate even more innovative applications of AI in healthcare, leading to better diagnostic tools and improved patient care.
💬 Your comments
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A Genetic algorithm aided hyper parameter optimization based ensemble model for respiratory disease prediction with Explainable AI.
Abstract
In the current era, a lot of research is being done in the domain of disease diagnosis using machine learning. In recent times, one of the deadliest respiratory diseases, COVID-19, which causes serious damage to the lungs has claimed a lot of lives globally. Machine learning-based systems can assist clinicians in the early diagnosis of the disease, which can reduce the deadly effects of the disease. For the successful deployment of these machine learning-based systems, hyperparameter-based optimization and feature selection are important issues. Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. Moreover, to enhance the efficacy of the predictions made by hyperparameter-optimized machine learning models, an ensemble model is proposed using a stacking classifier. Also, explainable AI was incorporated to define the feature importance by making use of Shapely adaptive explanations (SHAP) values. For the experimentation, the publicly accessible Mexico clinical dataset of COVID-19 was used. The results obtained show that the proposed model has superior prediction accuracy in comparison to its counterparts. Moreover, among all the hyperparameter-optimized algorithms, adaboost algorithm outperformed all the other hyperparameter-optimized algorithms. The various performance assessment metrics, including accuracy, precision, recall, AUC, and F1-score, were used to assess the results.
Author: [‘Kaur BP’, ‘Singh H’, ‘Hans R’, ‘Sharma SK’, ‘Sharma C’, ‘Hassan MM’]
Journal: PLoS One
Citation: Kaur BP, et al. A Genetic algorithm aided hyper parameter optimization based ensemble model for respiratory disease prediction with Explainable AI. A Genetic algorithm aided hyper parameter optimization based ensemble model for respiratory disease prediction with Explainable AI. 2024; 19:e0308015. doi: 10.1371/journal.pone.0308015