A machine learning‐based risk scoring system for infertility considering different age groups. Issue 3 (21st December 2020)

• Record Type: Journal Article
• Authors: Liao, ShuJie; Jin, Lei; Dai, Wan‐Qiang; Huang, Ge; Pan, Wulin; Hu, Cheng; Pan, Wei
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 3(2021)
• Page Start: 1331
• Page End: 1344
• Publication Date: 2020-12-21
• Subjects: entropy‐based feature discretization -- infertility -- precision medicine -- random forest -- risk scoring; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22344
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: The application of artificial intelligence (AI) methods in medical field is increasing year by year; however, few studies have applied AI methods in the reproductive field. In view of the complexity of infertility diagnosis and treatment, a machine learning‐based risk scoring system for infertility was constructed in this paper to help clinicians better grasp the patient's condition. First, eight key features of infertility are screened out by feature selection. Second, the entropy‐based feature discretization method was used to divide the feature abnormal intervals, and the random forest was used to determine the weight of each feature. Finally, the pregnancy outcome can be predicted according to the overall risk score of patients, which is helpful for doctors to choose targeted treatment more efficiently. It is worth noting that, to further improve the accuracy of the diagnosis, we also divided the patients into age groups and constructed the corresponding risk scoring system for patients of different age groups. The stability test results show the good performance of the system. The risk scoring system for infertility built in this paper is a meaningful exploration of the application of AI in the field of reproduction.