ARCHIVES

1. Ganie, S. M., Dutta Pramanik, P. K., & Zhao, Z. (2024). Improved liver disease prediction from clinical data through an evaluation of
ensemble learning approaches. BMC Medical Informatics and Decision Making, 24(1), 160.
2. Al Ahad, A., Das, B., Khan, M. R., Saha, N., Zahid, A., & Ahmad, M. (2024). Multiclass liver disease prediction with adaptive data
preprocessing and ensemble modeling. Results in Engineering, 22, 102059.
3. Islam, R., Sultana, A., & Tuhin, M. N. (2024). A comparative analysis of machine learning algorithms with tree-structured parzen estimator
for liver disease prediction. Healthcare Analytics, 6, 100358.
4. Zhang, Z., Wang, S., Zhu, Z., & Nie, B. (2023). Identification of potential feature genes in non-alcoholic fatty liver disease using
bioinformatics analysis and machine learning strategies. Computers in biology and medicine, 157, 106724.
5. Lanjewar, M. G., Parab, J. S., Shaikh, A. Y., & Sequeira, M. (2023). CNN with machine learning approaches using ExtraTreesClassifier and
MRMR feature selection techniques to detect liver diseases on cloud. Cluster Computing, 26(6), 3657-3672.
6. Takahashi, Y., Dungubat, E., Kusano, H., & Fukusato, T. (2023). Artificial intelligence and deep learning: New tools for histopathological
diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. Computational and Structural Biotechnology Journal, 21, 2495-
2501.
7. Aslam, M. H., Hussain, S. F., & Ali, R. H. (2022, November). Predictive analysis on severity of non-alcoholic fatty liver disease (nafld)
using machine learning algorithms. In 2022 17th International Conference on Emerging Technologies (ICET) (pp. 95-100). IEEE.
8. Dalal, S., Onyema, E. M., & Malik, A. (2022). Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better
accuracy. World Journal of Gastroenterology, 28(46), 6551.
9. Che, H., Brown, L. G., Foran, D. J., Nosher, J. L., & Hacihaliloglu, I. (2021). Liver disease classification from ultrasound using multi-scale
CNN. International Journal of Computer Assisted Radiology and Surgery, 16(9), 1537-1548.
10. Okanoue, T., Shima, T., Mitsumoto, Y., Umemura, A., Yamaguchi, K., Itoh, Y., ... & Harada, K. (2021). Artificial intelligence/neural network
system for the screening of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatology Research, 51(5), 554-569.
11. Su, T. H., Wu, C. H., & Kao, J. H. (2021). Artificial intelligence in precision medicine in hepatology. Journal of Gastroenterology and
Hepatology, 36(3), 569-580.
12. Nahar, N., Ara, F., Neloy, M. A. I., Barua, V., Hossain, M. S., & Andersson, K. (2019, December). A comparative analysis of the ensemble
method for liver disease prediction. In 2019 2nd international conference on innovation in engineering and technology (ICIET) (pp. 1-6).
IEEE.
13. Arbain, A. N., & Balakrishnan, B. Y. P. (2019). A comparison of data mining algorithms for liver disease prediction on imbalanced
data. International Journal of Data Science and Advanced Analytics, 1(1), 1-11
14. LaPierre, N., Ju, C. J. T., Zhou, G., & Wang, W. (2019). MetaPheno: a critical evaluation of deep learning and machine learning in
metagenome-based disease prediction. Methods, 166, 74-82