Prediction of disease progression of chronic hepatitis C based on XGBoost algorithm

Ma, Ling; Yang, Yongsheng; Ge, Xin; Wan, Yi-dan; Sang, Xin Zhu

doi:10.1109/icris52159.2020.00151

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…Based on the experimental results, SVM and XGBoost techniques can be used as efficient tools for doctors and specialists using routine and inexpensive blood test data to predict hepatitis C. Through the years, machine learning techniques have been used in disease prediction, such as with hepatitis C, by various researchers. L. Ma et al [ 8 ] established various classification models on 615 individuals’ data to predict hepatitis C patients. In their study, the XGBoost algorithm outperforms other models with an accuracy of 91.56%.…”

Section: Discussionmentioning

confidence: 99%

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Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques

2023

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Hepatitis C is a liver infection caused by the hepatitis C virus (HCV). Due to the late onset of symptoms, early diagnosis is difficult in this disease. Efficient prediction can save patients before permeant liver damage. The main objective of this study is to employ various machine learning techniques to predict this disease based on common and affordable blood test data to diagnose and treat patients in the early stages. In this study, six machine learning algorithms (Support Vector Machine (SVM), K-nearest Neighbors (KNN), Logistic Regression, decision tree, extreme gradient boosting (XGBoost), artificial neural networks (ANN)) were utilized on two datasets. The performances of these techniques were compared in terms of confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC), and the area under the curve (AUC) to identify a method that is appropriate for predicting this disease. The analysis, on NHANES and UCI datasets, revealed that SVM and XGBoost (with the highest accuracy and AUC among the test models, >80%) can be effective tools for medical professionals using routine and affordable blood test data to predict hepatitis C.

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Section: Discussionmentioning

confidence: 99%

“…Several studies have utilized machine learning algorithms to predict and diagnose hepatitis C. Ma et al [ 8 ] established various classification models and found that the XGBoost algorithm had the best accuracy (91.56%). Ahammed et al [ 9 ] implemented three machine learning algorithms and found that KNN had the best accuracy (94.40%).…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques

2023

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“…Hepatitis C has been predicted and diagnosed in several studies using machine learning algorithms. Ma et al [39] developed a number of classification models and determined that the XGBoost method achieved the highest accuracy (91.56%). Three machine learning methods were used by Ahammed et al [40], and the greatest accuracy (94.40%) was obtained using KNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Hepatitis C prediction using SVM, logistic regression and decision tree

Anjuman Ara,

Anhar Sami,

Daniel Lucky Michael

et al. 2024

World J. Adv. Res. Rev.

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Hepatitis C is an infection of the liver brought on by the HCV virus. In this condition, early diagnosis is challenging because of the delayed onset of symptoms. Predicting well enough can spare patients from permeant liver damage. The primary goal of this work is to use several machine learning methods to forecast this disease based on widely available and reasonably priced blood test data in order to diagnose and treat patients early on. Three machine learning techniques support vector machine (SVM), logistic regression, decision tree, has been applied on one dataset in this work. To find a suitable approach for illness prediction, the confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC), performances of different strategies have been assessed. The SVM model's overall accuracy is 0.92, the highest among the three models.

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“…Infections spread by these ways are now extremely rare in the United States, thanks to the widespread use of routine blood supply testing ( 3 ). The medical experts have highly recommended the screening of hepatitis C even for people not showing any symptoms of this disease as after analyzing the population the researchers have concluded that people suffering from chronic hepatitis C may doesn't show any symptoms until it causes complications ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease

Onyema

Dalal

Dhaou

et al. 2022

Front. Public Health

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Machine learning algorithms are excellent techniques to develop prediction models to enhance response and efficiency in the health sector. It is the greatest approach to avoid the spread of hepatitis C, especially injecting drugs, is to avoid these behaviors. Treatments for hepatitis C can cure most patients within 8 to 12 weeks, so being tested is critical. After examining multiple types of machine learning approaches to construct the classification models, we built an AI-based ensemble model for predicting Hepatitis C disease in patients with the capacity to predict advanced fibrosis by integrating clinical data and blood biomarkers. The dataset included a variety of factors related to Hepatitis C disease. The training data set was subjected to three machine-learning approaches and the validated data was then used to evaluate the ensemble learning-based prediction model. The results demonstrated that the proposed ensemble learning model has been observed ad more accurate compared to the existing Machine learning algorithms. The Multi-layer perceptron (MLP) technique was the most precise learning approach (94.1% accuracy). The Bayesian network was the second-most accurate learning algorithm (94.47% accuracy). The accuracy improved to the level of 95.59%. Hepatitis C has a significant frequency globally, and the disease's development can result in irreparable damage to the liver, as well as death. As a result, utilizing AI-based ensemble learning model for its prediction is advantageous in curbing the risks and improving treatment outcome. The study demonstrated that the use of ensemble model presents more precision or accuracy in predicting Hepatitis C disease instead of using individual algorithms. It also shows how an AI-based ensemble model could be used to diagnose Hepatitis C disease with greater accuracy.

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Prediction of disease progression of chronic hepatitis C based on XGBoost algorithm

Cited by 6 publications

References 3 publications

Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques

Performance Comparison of Machine Learning Approaches on Hepatitis C Prediction Employing Data Mining Techniques

Hepatitis C prediction using SVM, logistic regression and decision tree

Artificial Intelligence-Based Ensemble Learning Model for Prediction of Hepatitis C Disease

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