Enhancing Liver Disease Detection Using Cloud Computing And Autoencoders In Healthcare Systems
Keywords:
Liver Disease, Cloud Infrastructure, Deep Belief Network, Autoencoders, Healthcare SystemsAbstract
Liver disease is a dangerous medical condition in need of an early diagnosis for proper treatment to prevent complications. This paper presents a prediction framework for liver disease based on Autoencoders and Cloud Infrastructure. The framework commences with the preprocessing of the data through cleaning, augmentation, and Z-score scaling, after which feature extraction from a Deep Belief Network (DBN) and Autoencoder-based classification are performed. The performance of the proposed model is evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The Proposed Autoencoders model performs better than the existing methods like Ensemble Method, HGB+MARS+SoftMax and SVC as per the experiment results. The Proposed Autoencoders have excellent 99.45% accuracy, 95.11% precision, 96.31% recall, 97.82% F1-score, and 96.51% AUC-ROC, and they exhibit superior performance in predicting liver disease. The suggested framework, with its cloud infrastructure and deep learning architecture, provides a strong solution for accurate and efficient diagnosis of liver disease.
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