## 5 Conclusions  
The purpose of this research is to compare the efficacy of several neural network models for identifying liver illnesses, such as CNN, RNN, and LSTM. The outcomes of the study show that the CNN+LSTM model may achieve superior performance compared to the other models in terms of recall, precision, AUC-ROC, and F1 score. The results of this study indicate that the integration of LSTM and CNN models has the potential to enhance the precision and resilience of liver disease diagnosis. In the future, further studies on the CNN+LSTM framework can be conducted on its generalizability and suitability for the analysis of diverse liver disease datasets. This will allow us to identify its potential applications in different clinical scenarios. The development of interpretability methods for the CNN+LSTM model can help improve the model's comprehension of the decision-making process, allowing researchers and clinicians to gain a deeper understanding of  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC-ROC** \\ \hline CNN & 96.25 & 96 & 95 & 95 & 97 \\ RNN & 92.81 & 92 & 91 & 91 & 94 \\ LSTM & 94.63 & 94 & 93 & 93 & 96 \\ CNN+LSTM (Proposed) & 98.73 & 99 & 98 & 98 & 99 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Applied model with parameters and accuracy statistics.  
Figure 7: Comparison of various DL models with proposed model.  
liver disease. By unraveling the model's temporal patterns and features, researchers and clinicians may learn more about the underlying mechanisms of the condition. In addition, the model can be further expanded and its performance improved by incorporating multi-modal information sources, such as genetic data and laboratory test results. This enhancement would enable the system to achieve higher levels of accuracy and efficiency in the detection of liver disorders. We performed a comprehensive study on the applied tools and techniques of several researchers [9; 16; 17; 19; 20; 21; 24; 30; 31; 32; 33; 34; 35; 36] and assessed their performances using machine learning methodologies. We demonstrated the advantages and shortcomings of each of these techniques used by researchers, as shown in Table 4. The CNN+LSTM model, as proposed, has enhanced performance in liver disease recognition when compared to alternative models. The study's results indicate that the use of deep learning methods can improve both the accuracy and effectiveness of medical diagnostics in detecting liver illnesses. These advancements in technology have the potential to enhance patient outcomes and facilitate the development of personalized treatment plans.  
Conceptualization, M.A.H.; Methodology, M.A.H. and S.L.; Validation, M.R.; Formal analysis, A.M.H., M.A.H., N.A.M., S.L., B.M.E. and M.R.; Investigation, A.M.H., M.A.H., N.A.M., S.L., B.M.E. and M.R.; Resources, N.A.M.; Writing--original draft, M.A.H.; Writing--review & editing, A.M.H., M.A.H., S.L., B.M.E. and M.R.; Funding acquisition, A.M.H. All authors have read and agreed to the published version of the manuscript. The authors extend their appreciation to the Deanship of research and innovation, Jazan University, Kingdom of Saudi Arabia for supporting and funding the project work. The Grant Number of this project is ISP23-99. The authors declare no conflicts of interest. The authors declare no conflicts of interest.