⚡ Quick Summary
This study introduces a novel deep learning framework, R-DLH2O, designed for forecasting cardiovascular crises by leveraging big data and advanced pre-processing techniques. The framework demonstrates impressive performance metrics, including an accuracy of 95.93% and a processing time of just 436 seconds.
🔍 Key Details
- 📊 Dataset: Utilizes large datasets for training
- 🧩 Features used: Robust pre-processing, feature selection
- ⚙️ Technology: R-DLH2O framework with H2O for big data processing
- 🏆 Performance: Accuracy 95.93%, Precision 92.57%, Recall 93.6%
🔑 Key Takeaways
- 📊 R-DLH2O is a multi-phase framework for predicting cardiovascular crises.
- 💡 Incorporates robust pre-processing techniques to handle noisy data effectively.
- 🤖 Modified Whale Optimization Algorithm (MWOA) enhances feature selection and performance.
- 🏆 Outperformed other heuristic algorithms in speed, showcasing its efficiency.
- 🌍 Framework processing time is remarkably low at 436 seconds.
- 📈 Mean per-class error recorded at 0.150125, indicating high reliability.
- 🔍 Validated through six performance tests demonstrating its robustness.
- 📚 Study published in Sci Rep, highlighting its significance in healthcare analytics.
📚 Background
The integration of big data and deep learning in healthcare has opened new avenues for improving patient outcomes, particularly in predicting critical health events like cardiovascular crises. However, the challenge lies in managing the noisy data often encountered in real-world scenarios. This study addresses these challenges by proposing a robust framework that enhances data quality and predictive accuracy.
🗒️ Study
Conducted by a team of researchers, the study aimed to develop a comprehensive framework, R-DLH2O, that integrates five distinct phases: robust pre-processing, feature selection, feed-forward neural network, prediction, and performance evaluation. This structured approach is designed to ensure that the framework can effectively handle the complexities of big data while maintaining high performance in crisis prediction.
📈 Results
The results from the implementation of the R-DLH2O framework were promising. The framework achieved an impressive accuracy of 95.93%, with a mean per-class error of 0.150125. Additionally, the precision and recall rates were recorded at 92.57% and 93.6%, respectively. These metrics indicate a high level of reliability and effectiveness in predicting cardiovascular crises.
🌍 Impact and Implications
The implications of this research are significant for the field of healthcare analytics. By providing a robust framework for forecasting cardiovascular crises, R-DLH2O has the potential to enhance clinical decision-making and improve patient outcomes. The integration of advanced algorithms and big data processing can lead to more timely interventions, ultimately saving lives and optimizing healthcare resources.
🔮 Conclusion
This study highlights the transformative potential of deep learning and big data in healthcare. The R-DLH2O framework not only addresses the challenges posed by noisy data but also sets a new standard for accuracy and efficiency in crisis prediction. As we continue to explore the intersection of technology and healthcare, frameworks like R-DLH2O will play a crucial role in shaping the future of patient care.
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Optimized robust learning framework based on big data for forecasting cardiovascular crises.
Abstract
Numerous Deep Learning (DL) scenarios have been developed for evolving new healthcare systems that leverage large datasets, distributed computing, and the Internet of Things (IoT). However, the data used in these scenarios tend to be noisy, necessitating the incorporation of robust pre-processing techniques, including data cleaning, preparation, normalization, and addressing imbalances. These steps are crucial for generating a robust dataset for training. Designing frameworks capable of handling such data without compromising efficiency is essential to ensuring robustness. This research aims to propose a novel healthcare framework that selects the best features and enhances performance. This robust deep learning framework, called (R-DLH2O), is designed for forecasting cardiovascular crises. Unlike existing methods, R-DLH2O integrates five distinct phases: robust pre-processing, feature selection, feed-forward neural network, prediction, and performance evaluation. This multi-phase approach ensures superior accuracy and efficiency in crisis prediction, offering a significant advancement in healthcare analytics. H2O is utilized in the R-DLH2O framework for processing big data. The main improvement of this paper lies in the unique form of the Whale Optimization Algorithm (WOA), specifically the Modified WOA (MWOA). The Gaussian distribution approach for random walks was employed with the diffusion strategy to choose the optimal MWOA solution during the growth phase. To validate the R-DLH2O framework, six performance tests were conducted. Surprisingly, the MWOA-2 outperformed other heuristic algorithms in speed, despite exhibiting lower accuracy and scalability. The suggested MWOA was further analyzed using benchmark functions from CEC2005, demonstrating its advantages in accuracy and robustness over WOA. These findings highlight that the framework’s processing time is 436 s, mean per-class error is 0.150125, accuracy 95.93%, precision 92.57%, and recall 93.6% across all datasets. These findings highlight the framework’s potential to produce significant and robust results, outperforming previous frameworks concerning time and accuracy.
Author: [‘Elseddeq NG’, ‘Elghamrawy SM’, ‘Eldesouky AI’, ‘Salem MM’]
Journal: Sci Rep
Citation: Elseddeq NG, et al. Optimized robust learning framework based on big data for forecasting cardiovascular crises. Optimized robust learning framework based on big data for forecasting cardiovascular crises. 2024; 14:28224. doi: 10.1038/s41598-024-76569-6