Machine Learning Model for Hepatitis C Diagnosis Customized to Each Patient

Leran Chen1; Ping Ji2; Yongsheng Ma1

2022

10.1109/ACCESS.2022.3210347

IEEE Access   Impact Factor & Quartile

2169-3536

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.

Machine learning custom model hepatitis C disease diagnosis data augmentation parameter optimization

SCI

English

First

Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong[RK3L]

Computer Science ; Engineering ; Telecommunications

Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications

WOS:000866430500001

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Web of Science

1.Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China
2.Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China

Leran Chen,Ping Ji,Yongsheng Ma. Machine Learning Model for Hepatitis C Diagnosis Customized to Each Patient[J]. IEEE Access,2022,10:106655-106672.

Leran Chen,Ping Ji,&Yongsheng Ma.(2022).Machine Learning Model for Hepatitis C Diagnosis Customized to Each Patient.IEEE Access,10,106655-106672.

Leran Chen,et al."Machine Learning Model for Hepatitis C Diagnosis Customized to Each Patient".IEEE Access 10(2022):106655-106672.