Early Detection of Liver Disease and Precise Health Optimization using Advanced Computational Models

The progressive nature of liver disease makes it a worrying factor globally and is often detected too late, where treatment options are sparse. As with most diseases, early detection makes a significant difference to the outcomes a patient faces and the resources that need to be devoted by the healthcare system. The purpose of this paper is to propose a predictive model for liver disease detection with the implementation of Deep Learning TabNet architecture, which is specialized in analyzing structured data. In contrast to other models, TabNet uses a novel approach that improves feature selection and overall accuracy through the use of attention mechanisms. The system’s non-stop surveillance of patient data enables it to report irregularities followed by real-time alerts for timely action to be taken. Additionally, batch data processing allows for a thorough analysis of the data that captures various trends for proactive disease control. The enhancement of deep learning for the predictive factors of liver disease is expected to strengthen preventive medicine and make further strides in AI diagnostics and patient care. The proposed TabNet model achieves a recall of 0.991, precision of 0.992, and an F1 score of 0.991 at a learning rate of 0.0015, demonstrating exceptional performance in accurately identifying liver disease.