Comparison of Machine Learning Classification Methods in Hepatitis C Virus

Syafaah, Lailis; Zulfatman, Zulfatman; Pakaya, Ilham; Lestandy, Merinda

doi:10.15575/join.v6i1.719

Cited by 22 publications

(12 citation statements)

References 19 publications

(28 reference statements)

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“…L. Syafa’ah et al [ 14 ] evaluated the algorithms KNN, naive Bayes, neural networks, and RF for detection of hepatitis C. The results demonstrated that NN’s accuracy can reach 95.12%, higher than naive Bayes, KNN, and RF. Another study, by Oleiwi et al [ 15 ], used four machine learning techniques (KNN, SVM, naive Bayes, and decision tree).…”

Section: Discussionmentioning

confidence: 99%

“…El-Salam et al [ 12 ] used multiple-classifier models and achieved accuracy rates ranging from 65.6% to 68.9%. Hashem et al [ 13 ] applied several machine learning approaches and found an accuracy range of 66.3% to 84.4% for predicting advanced chronic hepatitis C. Syafa’ah et al [ 14 ] evaluated multiple algorithms and found that neural networks had the highest accuracy (95.12%). Oleiwi et al [ 15 ] used four machine learning techniques and found that the decision tree method had the best accuracy (93.44%) for classifying and diagnosing hepatitis C. This study aims to choose the best algorithms for predicting hepatitis C based on routine and inexpensive blood test data.…”

Section: Introductionmentioning

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%

Section: Introductionmentioning

confidence: 99%

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

2023

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“…But the main disadvantage of Logistic regression is over tting and multicollinearity. It is tough to extract the complex patterns in the given data set with highest accuracy [12].…”

Section: Related Materials and Methodsmentioning

confidence: 99%

Dho-gml Optimization Approach for Thyroid Classification Using Artificial Intelligence

Dhivya¹,

Bazilabanu²

2022

Preprint

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Disease classification using Artificial Intelligence (AI) is one of the emerging areas for medical professionals to diagnose the disease. Thyroid is a common disease which is faced by most of the people that produce severe health problems. As a second opinion to medical professionals, various classification models are used such as Logistic Regression, Decision Tree, Random Forest, K Nearest Neighbor and gradient boosting algorithms.An Artificial Neural Network (ANN) is a part of AI mainly used to identify the nonlinear relationship among the features for better prediction. However, there are some common problems like under fitting and overfitting that occur during the analysis and prediction. The present study suggests the Deep Hyper Optimization (DHO) - Genetic Meta Learning (GML) technique to increase the accuracy and reduce the elapsed time of execution without overfitting. In the first phase, DHO is used to fine tune the weight, bias and identify the optimal number of parameters in the hidden layer using DHO. The performance of various algorithms are observed and compared with Deep Hyper Optimization (DHO) technique. It chooses the best parameter for classification based on the highest probability of particular value in the parameters after performing the divide and conquer approach. In the second phase, GML technique is used to identify the optimal model for classification. It uses the crossover to select the best model and generate offspring population. The performance of each method is observed based on the accuracy, elapsed time, precision, recall, and F1 score, FPR, FNR, TNR and Area under Curve (AUC).

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“…Penerapan model lain dengan menggunakan pendekatan deep learning yaitu artificial neural network (ANN) untuk mendeteksi hepatitis C mampu menghasilkan akurasi 97,78% [9]. Study lain membahas tentang evaluasi penerapan berbagai algoritma klasifikasi machine learning untuk deteksi virus hepatitis C, algoritma tersebut adalah naïve bayes, random forest, dan KNN [10].…”

Section: Pendahuluanunclassified

Analisis Penerapan Metode Ensembled Learning Decision Tree Pada Klasifikasi Virus Hepatitis C

Charisma¹,

Pamungkas²,

Saputra³

et al. 2022

JoSYC

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Hepatitis C virus is a deadly virus that attacks the liver. This virus can cause chronic infections, even 80% of sufferers have experienced an illness. To minimize the risk of exposure to disease caused by the hepatitis C virus, consultation with a doctor or using an intelligent detection system can be conducted. Of course, if used a smart strategy, our need data that already contains parameters related to hepatitis C. This study uses a public dataset that the public can access. So, the purpose of this study is to classify patients with hepatitis C virus using a tree-based algorithm. The results obtained by applying the proposed algorithm are 93% accuracy, 92% precision, and 91% recall. This study also performs comparisons with other methods, namely naive bayes. The results show that the tree-based way is superior.

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Comparison of Machine Learning Classification Methods in Hepatitis C Virus

Cited by 22 publications

References 19 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

Dho-gml Optimization Approach for Thyroid Classification Using Artificial Intelligence

Analisis Penerapan Metode Ensembled Learning Decision Tree Pada Klasifikasi Virus Hepatitis C

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