Hybrid XGBoost model with hyperparameter tuning for prediction of liver disease with better accuracy  

作　　者：Surjeet Dalal Edeh Michael Onyema Amit Malik 

机构地区：[1]Department of CSE,Amity University,Gurugram 122413,Haryana,India [2]Department of Mathematics and Computer Science,Coal City University,Enugu 400102,Nigeria [3]Department of CSE,SRM University,Delhi-NCR,Sonipat 131001,Haryana,India

出　　处：《World Journal of Gastroenterology》2022年第46期6551-6563,共13页世界胃肠病学杂志（英文版）

摘　　要：BACKGROUND Liver disease indicates any pathology that can harm or destroy the liver or prevent it from normal functioning.The global community has recently witnessed an increase in the mortality rate due to liver disease.This could be attributed to many factors,among which are human habits,awareness issues,poor healthcare,and late detection.To curb the growing threats from liver disease,early detection is critical to help reduce the risks and improve treatment outcome.Emerging technologies such as machine learning,as shown in this study,could be deployed to assist in enhancing its prediction and treatment.AIM To present a more efficient system for timely prediction of liver disease using a hybrid eXtreme Gradient Boosting model with hyperparameter tuning with a view to assist in early detection,diagnosis,and reduction of risks and mortality associated with the disease.METHODS The dataset used in this study consisted of 416 people with liver problems and 167 with no such history.The data were collected from the state of Andhra Pradesh,India,through https://www.kaggle.com/datasets/uciml/indian-liver-patientrecords.The population was divided into two sets depending on the disease state of the patient.This binary information was recorded in the attribute"is_patient".RESULTS The results indicated that the chi-square automated interaction detection and classification and regression trees models achieved an accuracy level of 71.36%and 73.24%,respectively,which was much better than the conventional method.The proposed solution would assist patients and physicians in tackling the problem of liver disease and ensuring that cases are detected early to prevent it from developing into cirrhosis(scarring)and to enhance the survival of patients.The study showed the potential of machine learning in health care,especially as it concerns disease prediction and monitoring.CONCLUSION This study contributed to the knowledge of machine learning application to health and to the efforts toward combating the problem of liver disease.Howe

关 键 词：Liver infection Machine learning Chi-square automated interaction detection Classification and regression trees Decision tree XGBoost Hyperparameter tuning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] R575[自动化与计算机技术—控制科学与工程]