Data-Centric Approach to Hepatitis C Virus Severity Prediction

被引：1

作者：

Sharma, Aniket ^{[1
]}

Arora, Ashok ^{[1
]}

Gupta, Anuj ^{[1
]}

Singh, Pramod Kumar ^{[1
]}

机构：

[1] ABV Indian Inst Informat Technol & Management, Computat Intelligence & Data Min Res Lab, Gwalior, Madhya Pradesh, India

来源：

INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, ISDA 2021 | 2022年 / 418卷

关键词：

Hepatitis C virus; Artificial intelligence; Machine learning; Filter-based statistical feature selection; Cross-validation;

D O I：

10.1007/978-3-030-96308-8_39

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Every year, around 1.5 million of the world population succumbs to the Hepatitis C Virus. 70% of these cases develop chronic infection and cirrhosis within the next 20 years. Because there is no effective treatment for HCV, it is critical to predicting the virus in its early stages. The study's goal is to define a data-driven approach for accurately detecting HCV severity in patients. Our approach achieves the highest accuracy of 86.79% compared to 70.89% using the standard approach.

引用

页码：421 / 431

页数：11

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