Enhanced Predictive Modeling for Non-Invasive Liver Disease Diagnosis

Donni Prabowo; Bety Wulan Sari; Yoga Pristyanto; Afrig Aminuddin

doi:10.29207/resti.v9i4.6449

Authors

Donni Prabowo Universitas AMIKOM Yogyakarta
Bety Wulan Sari Universitas AMIKOM Yogyakarta
Yoga Pristyanto Universitas AMIKOM Yogyakarta
Afrig Aminuddin Universitas AMIKOM Yogyakarta

DOI:

https://doi.org/10.29207/resti.v9i4.6449

Keywords:

CatBoost, Liver disease classification, Machine learning, Predictive modeling

Abstract

Liver diseases (e.g. cirrhosis, hepatitis, and fatty liver disease) are globally one of the leading causes of mortality and are typically diagnosed in advanced stages due to vague symptoms and the difficulty involved in existing diagnostic techniques (e.g. biopsies). To optimize the early diagnosis of liver disease, this study proposes an enhanced, non-invasive approach using machine learning techniques. The research is enriched with a full pipeline, from exploratory data analysis and imputation of the dataset, treatment of the outlier, encoding of labels and scaling using ILPD (Indian Liver Patient Dataset). The classification models compared were RandomForest, XGBoost, LGBM, and CatBoost. The CatBoost algorithm fine-tuned with RandomizedSearchCV showed the highest performance with a test accuracy of 93%. The performance was again better than any already published methods showing that advanced ensembling and hyperparameter optimization worked. The proposed model is suitable for incorporation into clinical decision support systems and provides reliable and accurate diagnostic assistance. In addition to its high accuracy, the model is robust for missing and categorical data, which is a challenge in any real-world clinical scenario. These findings add to the growing body of evidence supporting AI-based medical diagnostics and suggest that CatBoost is a highly promising tool for facilitating timely screening and diagnosis of liver disease. Furthermore, the study stresses the need for thorough preprocessing and cross-validation, which serve to reduce biases that are present in widely applied datasets. Ongoing future efforts may involve the integration of multi-source data and implementation of explainable AI techniques to allow for wider clinical trust and use.

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References

M.-C. Guan et al., “Early diagnosis and therapeutic strategies for hepatocellular carcinoma: From bench to bedside,” World J. Gastrointest. Oncol., vol. 13, no. 4, pp. 197–215, Apr. 2021, doi: 10.4251/wjgo.v13.i4.197.

H. Wazir et al., “Diagnosis and Treatment of Liver Disease: Current Trends and Future Directions,” Cureus, Dec. 2023, doi: 10.7759/cureus.49920.

Z. Yao, J. Li, Z. Guan, Y. Ye, and Y. Chen, “Liver disease screening based on densely connected deep neural networks,” Neural Networks, vol. 123, 2020, doi: 10.1016/j.neunet.2019.11.005.

S. A. Alowais et al., “Revolutionizing healthcare: the role of artificial intelligence in clinical practice,” BMC Med. Educ., vol. 23, no. 1, p. 689, Sep. 2023, doi: 10.1186/s12909-023-04698-z.

S. Maleki Varnosfaderani and M. Forouzanfar, “The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century,” Bioengineering, vol. 11, no. 4, p. 337, Mar. 2024, doi: 10.3390/bioengineering11040337.

D. A. Hashimoto, G. Rosman, D. Rus, and O. R. Meireles, “Artificial Intelligence in Surgery: Promises and Perils,” Ann. Surg., vol. 268, no. 1, pp. 70–76, Jul. 2018, doi: 10.1097/SLA.0000000000002693.

G. Chongo and J. Soldera, “Use of machine learning models for the prognostication of liver transplantation: A systematic review,” World J. Transplant., vol. 14, no. 1, Mar. 2024, doi: 10.5500/wjt.v14.i1.88891.

D. Nam, J. Chapiro, V. Paradis, T. P. Seraphin, and J. N. Kather, “Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction,” JHEP Reports, vol. 4, no. 4. 2022. doi: 10.1016/j.jhepr.2022.100443.

A. Alizargar, Y.-L. Chang, M. Alkhaleefah, and T.-H. Tan, “Precision Non-Alcoholic Fatty Liver Disease (NAFLD) Diagnosis: Leveraging Ensemble Machine Learning and Gender Insights for Cost-Effective Detection,” Bioengineering, vol. 11, no. 6, p. 600, Jun. 2024, doi: 10.3390/bioengineering11060600.

T. A. Assegie, R. Subhashni, N. K. Kumar, J. P. Manivannan, P. Duraisamy, and M. F. Engidaye, “Random forest and support vector machine-based hybrid liver disease detection,” Bull. Electr. Eng. Informatics, vol. 11, no. 3, 2022, doi: 10.11591/eei.v11i3.3787.

A. Spann et al., “Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review,” Hepatology, vol. 71, no. 3, pp. 1093–1105, Mar. 2020, doi: 10.1002/hep.31103.

N. Tanwar and K. F. Rahman, “Machine learning in liver disease diagnosis: Current progress and future opportunities,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1022, no. 1, 2021, doi: 10.1088/1757-899X/1022/1/012029.

M. Minnoor, “Liver Disease Diagnosis Using Machine Learning”, doi: 10.1109/aic.2022.08.

T. M. Ghazal, A. U. Rehman, M. Saleem, M. Ahmad, S. Ahmad, and F. Mehmood, “Intelligent Model to Predict Early Liver Disease using Machine Learning Technique,” 2022. doi: 10.1109/ICBATS54253.2022.9758929.

C. Geetha and A. R. Arunachalam, “Evaluation based Approaches for Liver Disease Prediction using Machine Learning Algorithms,” Jan. 2021. doi: 10.1109/ICCCI50826.2021.9402463.

N. Afreen, R. Patel, M. Ahmed, and M. Sameer, “A Novel Machine Learning Approach Using Boosting Algorithm for Liver Disease Classification,” 2021. doi: 10.1109/ISCON52037.2021.9702488.

S. S. Nigatu, P. C. R. Alla, R. N. Ravikumar, K. Mishra, G. Komala, and G. R. Chami, “A Comparitive Study on Liver Disease Prediction using Supervised Learning Algorithms with Hyperparameter Tuning,” in 2023 International Conference on Advancement in Computation and Computer Technologies, InCACCT 2023, 2023, pp. 353–357. doi: 10.1109/InCACCT57535.2023.10141830.

M. Ghosh et al., “A comparative analysis of machine learning algorithms to predict liver disease,” Intell. Autom. Soft Comput., vol. 30, no. 3, pp. 917–928, 2021, doi: 10.32604/iasc.2021.017989.

H. Hartatik, M. B. Tamam, and A. Setyanto, “Prediction for Diagnosing Liver Disease in Patients using KNN and Naïve Bayes Algorithms,” in 2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS), Oct. 2020, pp. 1–5. doi: 10.1109/ICORIS50180.2020.9320797.

J. Sadhasivam, S. J, G. R.M, and C. N, “Liver Disease Prediction Using Machine Learning Classification,” Webology, vol. 18, no. 02, pp. 441–452, Sep. 2021, doi: 10.14704/web/v18si02/web18293.

V. Mehta et al., “Machine Learning based Exploratory Data Analysis (EDA) and Diagnosis of Chronic Kidney Disease (CKD),” EAI Endorsed Trans. Pervasive Heal. Technol., vol. 10, Mar. 2024, doi: 10.4108/eetpht.10.5512.

N. Pandey, P. K. Patnaik, and S. Gupta, “Data Pre Processing for Machine Learning Models using Python Libraries,” Int. J. Eng. Adv. Technol., vol. 9, no. 4, pp. 1995–1999, Apr. 2020, doi: 10.35940/ijeat.D9057.049420.

K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Glob. Transitions Proc., vol. 3, no. 1, 2022, doi: 10.1016/j.gltp.2022.04.020.

B. Juba and H. S. Le, “Precision-Recall versus Accuracy and the Role of Large Data Sets,” Proc. AAAI Conf. Artif. Intell., vol. 33, no. 01, pp. 4039–4048, Jul. 2019, doi: 10.1609/aaai.v33i01.33014039.

C. N. Noviyanti and A. Alamsyah, “Early Detection of Diabetes Using Random Forest Algorithm,” J. Inf. Syst. Explor. Res., vol. 2, no. 1, Jan. 2024, doi: 10.52465/joiser.v2i1.245.

K. Budholiya, S. K. Shrivastava, and V. Sharma, “An optimized XGBoost based diagnostic system for effective prediction of heart disease,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 7, 2022, doi: 10.1016/j.jksuci.2020.10.013.

N. M. . R. -, V. S. -, S. T. -, and P. V. S. -, “Heart Disease Risk Assessment by Using Light GBM Technique,” Int. J. Multidiscip. Res., vol. 5, no. 2, 2023, doi: 10.36948/ijfmr.2023.v05i02.2620.

J. An, “Using CatBoost and Other Supervised Machine Learning Algorithms to Predict Alzheimer’s Disease,” in 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), Dec. 2022, pp. 1732–1739. doi: 10.1109/ICMLA55696.2022.00265.

K. S. R. Kundra, B. J. Lakshmi, I. V. S. Venugopal, and V. Guthula, “Flood Prediction using MLP, CATBOOST and Extra-Tree Classifier,” Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 7 s, 2023, doi: 10.17762/ijritcc.v11i7s.6974.

B. H. Cho, Y. H. Kim, K. B. Lee, Y. K. Hong, and K. C. Kim, “Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity,” Sensors, vol. 22, no. 12, 2022, doi: 10.3390/s22124378.

A. Abdalrada, Ali Fahem Neamah, and Hayder Murad, “Predicting Diabetes Disease Occurrence Using Logistic Regression: An Early Detection Approach,” Iraqi J. Comput. Sci. Math., vol. 5, no. 1, 2024, doi: 10.52866/ijcsm.2024.05.01.011.

C. Zhou et al., “A machine learning-based predictor for the identification of the recurrence of patients with gastric cancer after operation,” Sci. Rep., vol. 11, no. 1, 2021, doi: 10.1038/s41598-021-81188-6.

O. Bardhi and B. G. Zapirain, “Machine learning techniques applied to electronic healthcare records to predict cancer patient survivability,” Comput. Mater. Contin., vol. 68, no. 2, 2021, doi: 10.32604/cmc.2021.015326.