Published online Dec 14, 2022. doi: 10.3748/wjg.v28.i46.6551
Peer-review started: June 30, 2022
First decision: July 13, 2022
Revised: July 27, 2022
Accepted: November 21, 2022
Article in press: November 21, 2022
Published online: December 14, 2022
Processing time: 161 Days and 2.4 Hours
Liver disease is a leading cause of mortality in the United States and is regarded as a life-threatening condition across the world. It is possible for people to develop liver disease at a young age.
Predicting liver disease with precision, accuracy, and reliability can be accomplished through the use of a modified eXtreme Gradient Boosting model with hyperparameter tuning in comparison to the chi-square automated interaction detection (CHAID) and classification and regression tree models.
This study was conducted with the aim of fulfilling various objectives. The first objective was identifying the symptoms of liver disease and their impact on the patient. The authors studied various machine learning approaches for predicting liver disease and evaluated the performance of decision tree algorithms in prediction of liver disease. The next objective was to propose a modified eXtreme Gradient Boosting model with a hyperparameter tuning mechanism. Finally, the performance of the proposed model was validated with the existing models.
Hybrid eXtreme Gradient Boosting model with hyperparameter tuning was designed using data from patients who had liver disease and patients who were healthy.
The experimental results demonstrated that the accuracy level in the CHAID and classification and regression tree models were 71.36% and 73.24%, respectively. The proposed model was designed with the aim of gaining a sufficient level of accuracy. Hence, 93.65% accuracy was achieved in our proposed model.
The existing machine learning models, i.e. the CHAID model and the classification and regression tree model, do not achieve a high enough accuracy level. The proposed model predicted liver disease with 93.65% accuracy. This model has real-time adaptability and cost-effectiveness in liver disease prediction.
The proposed model can better predict liver-related disease by identifying the disease causes and suggesting better treatment options.