Detection of Hepatitis C Virus Progressed Patient’s Liver Condition Using Machine Learning

Ferdib-Al-Islam,; Akter, Laboni

doi:10.1007/978-981-16-2594-7_6

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…In this section, we compare the predictive performance of IHCP with existing methods, as shown in Table 5 . Akter et al used machine learning methods to classify normal individuals and patients with hepatitis C. LR performed the best with an accuracy of 95% [ 23 ]. Edeh et al [ 24 ] proposed the use of an ensemble learning predictive model to predict patients with hepatitis C. It achieved an accuracy of 95.59%.…”

Section: Resultsmentioning

confidence: 99%

IHCP: interpretable hepatitis C prediction system based on black-box machine learning models

Fan,

Lu,

Sun

2023

BMC Bioinformatics

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Background Hepatitis C is a prevalent disease that poses a high risk to the human liver. Early diagnosis of hepatitis C is crucial for treatment and prognosis. Therefore, developing an effective medical decision system is essential. In recent years, many computational methods have been proposed to identify hepatitis C patients. Although existing hepatitis prediction models have achieved good results in terms of accuracy, most of them are black-box models and cannot gain the trust of doctors and patients in clinical practice. As a result, this study aims to use various Machine Learning (ML) models to predict whether a patient has hepatitis C, while also using explainable models to elucidate the prediction process of the ML models, thus making the prediction process more transparent. Result We conducted a study on the prediction of hepatitis C based on serological testing and provided comprehensive explanations for the prediction process. Throughout the experiment, we modeled the benchmark dataset, and evaluated model performance using fivefold cross-validation and independent testing experiments. After evaluating three types of black-box machine learning models, Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, we adopted Bayesian-optimized RF as the classification algorithm. In terms of model interpretation, in addition to using common SHapley Additive exPlanations (SHAP) to provide global explanations for the model, we also utilized the Local Interpretable Model-Agnostic Explanations with stability (LIME_stabilitly) to provide local explanations for the model. Conclusion Both the fivefold cross-validation and independent testing show that our proposed method significantly outperforms the state-of-the-art method. IHCP maintains excellent model interpretability while obtaining excellent predictive performance. This helps uncover potential predictive patterns of the model and enables clinicians to better understand the model's decision-making process.

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Section: Resultsmentioning

confidence: 99%

IHCP: interpretable hepatitis C prediction system based on black-box machine learning models

Fan,

Lu,

Sun

2023

BMC Bioinformatics

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“…Safdari et al [ 30 ] applied data mining techniques to classify patients with suspected hepatitis C virus infection and achieved an accuracy of 90.3%. Akter et al [ 31 ] developed a machine learning model to detect the progression of hepatitis C virus in patients’ liver condition and achieved an accuracy of 91.67%. In comparison to the aforementioned six studies, our analysis on the NHANES and UCI datasets found that SVM and XGBoost (with AUC, >80%) can effectively predict hepatitis C using routine and affordable blood test data.…”

Section: Discussionmentioning

confidence: 99%

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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On leveraging self-supervised learning for accurate HCV genotyping

Fahmy,

Hammad,

Mabrouk

et al. 2024

Sci Rep

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Hepatitis C virus (HCV) is a major global health concern, affecting millions of individuals worldwide. While existing literature predominantly focuses on disease classification using clinical data, there exists a critical research gap concerning HCV genotyping based on genomic sequences. Accurate HCV genotyping is essential for patient management and treatment decisions. While the neural models excel at capturing complex patterns, they still face challenges, such as data scarcity, that exist a lot in computational genomics. To overcome this challenges, this paper introduces an advanced deep learning approach for HCV genotyping based on the graphical representation of nucleotide sequences that outperforms classical approaches. Notably, it is effective for both partial and complete HCV genomes and addresses challenges associated with imbalanced datasets. In this work, ten HCV genotypes: 1a, 1b, 2a, 2b, 2c, 3a, 3b, 4, 5, and 6 were used in the analysis. This study utilizes Chaos Game Representation for 2D mapping of genomic sequences, employing self-supervised learning using convolutional autoencoder for deep feature extraction, resulting in an outstanding performance for HCV genotyping compared to various machine learning and deep learning models. This baseline provides a benchmark against which the performance of the proposed approach and other models can be evaluated. The experimental results showcase a remarkable classification accuracy of over 99%, outperforming traditional deep learning models. This performance demonstrates the capability of the proposed model to accurately identify HCV genotypes in both partial and complete sequences and in dealing with data scarcity for certain genotypes. The results of the proposed model are compared to NCBI genotyping tool.

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Detection of Hepatitis C Virus Progressed Patient’s Liver Condition Using Machine Learning

Cited by 3 publications

References 15 publications

IHCP: interpretable hepatitis C prediction system based on black-box machine learning models

IHCP: interpretable hepatitis C prediction system based on black-box machine learning models

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

On leveraging self-supervised learning for accurate HCV genotyping

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