학술논문
Improve Accuracy in Healthcare Data Analysis using Competitive Ensemble Deep Learning Model
Document Type
Conference
Author
Source
2024 11th International Conference on Computing for Sustainable Global Development (INDIACom) Computing for Sustainable Global Development (INDIACom), 2024 11th International Conference on. :1792-1797 Feb, 2024
Subject
Language
Abstract
This paper discusses the significance of Machine Learning (ML) and Deep Learning (DL) techniques for structured and unstructured healthcare data. As healthcare data is increasing tremendously, it is difficult to identify hidden patterns in huge amounts of data. DL handles a massive amount of clinical data and provides better outcomes. A novel competitive ensemble deep learning model has been proposed to improve the classification performance of structured data. However, dealing with unstructured data, the proposed work highlights a competitive DL model for Twitter sentiment analysis. In addition, this paper discusses the proposed Competitive Ensemble Deep Learning (CEPL) algorithm for text data. The proposed model is compared with a traditional model to evaluate the model’s performance in the range of 0.2%-0.5%.