Machine Learning Model for Hepatitis C Diagnosis Customized to Each Patient

Abstract

Machine learning is now widely used in various fields, and it has made a big splash in the field of disease diagnosis. But traditional machine learning models are general-purpose, that is, one model is used to evaluate the health status of different patients. A general-purpose machine learning algorithm depends on a large amount of data and requires abundant computing power support, relies on the average level to describe the model performance, and cannot achieve optimal results on a specific problem. In this paper, we propose to train a unique model for each patient to improve the accuracy and ease of use of the model. The proposed approach to solving a problem in the paper is from three perspectives (1) targeted data processing, (2) model structure design: Passing in patient-related information into the model, and (3) hyperparameter tailored optimization. The preliminary experimental results show that using the custom model has advantages of high accuracy, high confidence, and low resource required to diagnose a patient. In the Hepatitis C dataset, over 99% accuracy and 94% recall were achieved using a smaller dataset (only 615 individuals' data) without knowledge of the relevant field. Traditional algorithms such as XGBoost or multi-algorithm ensemble could achieve less than 95% accuracy and only less than 70% recall. Out of a total of 56 patients, the custom model was able to identify 53 patients 20 more than traditional methods, bringing a new and efficient tool for future hepatitis C prevention and treatment efforts. INDEX TERMS Machine learning, custom model, hepatitis C, disease diagnosis, data augmentation, parameter optimization.

Novel graph-based machine-learning technique for viral infectious diseases: application to influenza and hepatitis diseases

“…Although the research provided useful insights, potential biases or missing data could distort the findings [ 20 ]. The main challenges include the need for substantial computing power and the difficulty of implementing and maintaining tailor-made models in real-world healthcare environments [ 21 ]. Enhanced diagnostic capabilities for hepatitis based on a few features and effective class balancing with the synthetic minority over-sampling technique (SMOTE) strategy were among the reported benefits [ 22 ].…”

The Development of Symbolic Expressions for the Detection of Hepatitis C Patients and the Disease Progression from Blood Parameters Using Genetic Programming-Symbolic Classification Algorithm

“…The results of the study showed that machine learning (ML) algorithms were able to predict advanced fibrosis in patients with AUCROC in the range 0.73-0.76 and with an accuracy of 66.3-84.4%, respectively. The CatBoost, XGBoost, RFGini, LightGBM, Random forest (RF), and KNN have been used in [11] to detect Hepatitis C patients The result showed that of all ML modes highest accuracy (0.9593), recall (0.6667), precision (1), and F1-score (0.7867) was achieved with XGBoost algorithm. In [12] the supervised learning (decision tree, logistic regression, KNN, Extreme Gradient Boosting, Gradient Boosting Machine, Gaussian Naive Bayes, RF, Gradient Boosting, SVM), and unsupervised learning (K-means, Hierarchical clustering, DBMSCN, Gaussian Mixture, and K-means) models were used to detect the Hepatitis C virus from a dataset containing laboratory data of Hepatitis C patients and blood donors.…”