Exploring Ovarian Cancer Prediction Models and Potential Markers Using Machine Learning.

Journal: Annals of clinical and laboratory science

PMID: 40340886

OBJECTIVE: To develop machine learning models, facilitate a more accurate diagnosis of ovarian cancer (OC), and explore potential markers.

Huijing Luo

Department of Clinical Laboratory Center, Tianjin Medical University General Hospital, Tianjin, China.
Xiaofang Zhang

School of Computer Science and Technology, Soochow University, Suzhou 215006, People's Republic of China.
Dongsha Shi

Department of Clinical Laboratory Center, Tianjin Medical University General Hospital, Tianjin, China.
Yanv Ren

Department of Clinical Laboratory Center, Tianjin Medical University General Hospital, Tianjin, China.
Wenyan Tian

Department of Obstetrics and Gynecology, Tianjin Medical University General Hospital, Tianjin, China.
Ruiyu Ma

Department of Prenatal Diagnostic Center and Fetal Medicine, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Zuoliang Dong

Department of Clinical Laboratory Center, Tianjin Medical University General Hospital, Tianjin, China dongzl_2006@163.com.

Adult Aged Area Under Curve Biomarkers, Tumor CA-125 Antigen Female Humans Machine Learning Middle Aged Neural Networks, Computer Ovarian Neoplasms ROC Curve Support Vector Machine

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