Machine learning methods to predict leprosy misdiagnosis in Yunnan Province, People's Republic of China
Yingwu Guo·Yu‐Ye Li·Hailong Yang·Xi Yang·Xiufeng Yu·Chunyu Zhang·Lijuan Zhou·Fushuai Zhao·Shengqing Lu·Qiaojing He·Lin Han·Weiwei Wang·Youhong Liu·Lijiao Yin
Background and objective Leprosy is a chronic infectious disease caused by Mycobacterium leprae that is often misdiagnosed. This study aimed to identify factors associated with leprosy misdiagnosis and develop and compare machine learning (ML) models to predict the risk of misdiagnosis. Methods A retrospective analysis was conducted on clinical and epidemiological data in 486 diagnosed leprosy patients. The outcome was a binary variable to indicate whether a patient had experienced a prior misdi