Competitive Ensemble Learning for Healthcare Data Analysis

In the rapidly evolving field of healthcare, the effective analysis of structured and unstructured data is crucial for enhancing disease diagnosis, patient outcomes, and overall healthcare management. This paper presents a novel approach, Competitive Ensemble Deep Learning (CEDL), designed to optimize healthcare data analysis by leveraging multiple deep learning models. Unlike traditional ensemble methods that combine weak and strong models, CEDL selectively integrates only the most effective models based on their performance, thereby improving classification accuracy and efficiency. The proposed method is tested on various healthcare datasets, demonstrating its superiority in handling both structured data, such as patient records, and unstructured data, including social media sentiment analysis. The results show significant improvements in predictive accuracy, making CEDL a robust and scalable solution for complex healthcare data analysis.