Optimizing Healthcare Resource Allocation Using Residual Convolutional Neural Networks
To optimize healthcare resource allocation using residual convolutional neural networks.
In the early stages, several traditional methods were adopted and implement-ed; however, the rise of AI and its technologies increased development in the healthcare sector and made it reach a better height in Industry 4.0.
In the early stages, several traditional methods were adopted and implemented; however, the rise of AI and its technologies increased development in the healthcare sector and made it reach a better height in Industry 4.0.
The main problem of this research is focusing on the inefficient allocation of healthcare resources, which leads to less outcome and accuracy. This research’s main novelty and objective is to implement a predictive model that may allocate resources based on several factors.
In the proposed method, Residual CNNs, a deep learning architecture well-known for its efficacy in image classification applications, we assess healthcare data and estimate ideal resource distribution. Residual CNNs are well-trained in the dataset on several factors and characteristics. The model produces predictions of resource allocation that maximize healthcare outcomes using comprehension of complex relationships and patterns in the data.
The novel feature of this work is the integration of the state-of-the-art deep learning architecture Residual CNNs into the domain of healthcare resource allocation. The proposed method, Residual CNNs, is well-trained in the dataset on several factors and characteristics. The model produces predictions of resource allocation that maximize healthcare outcomes by comprehending complex relationships and patterns in the data.
We show experimentally that the proposed approach effectively allocates healthcare resources. The residual CNN model outperforms traditional methods in accurately predicting resource allocation needs across different regions and demographic groups. We find significant increases in resource allocation efficiency by applying deep learning techniques, which enhance healthcare outcomes and reduce treatment disparities.
Investigations should prioritize the validation of the algorithm in various healthcare environments to assess its efficacy in clinical application.
This work can be enhanced in future research using several deep-learning algorithms to achieve better accuracy and performance.
Back