Prediction of Prognosis in Women with Infrtility due to Immune Causes Based on Cytokine Levels and Machine Learning

doi:10.13523/j.cb.2310050

China Biotechnology

2023, Vol. 43

Issue (12): 169-176 DOI: 10.13523/j.cb.2310050

Prediction of Prognosis in Women with Infrtility due to Immune Causes Based on Cytokine Levels and Machine Learning

YANG Jing¹,ZHONG Zi-xing²,NI Wan-mao^3,^**(

)

1 Laboratory Medicine Center, Department of Clinical Laboratory, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou 310014, China
2 Center for Reproductive Medicine, Department of Obstetrics, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou 310014, China
3 Cancer Center, Department of Pathology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou 310014, China

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Abstract

In order to investigate the difference of cytokines between immune infertility patients and healthy women, and the relationship between cytokine levels and treatment outcomes in immune infertility patients, 96 women with female immune infertility and 57 healthy women were enrolled as the experimental and control groups, respectively. The levels of 7 cytokines were measured by flow cytometry, and fertility status was followed for 2 years. The results showed that there were significant differences in the serum levels of 6 cytokines including IL-2, IL-4, IL-6, IL-10, TNF-α and IFN-γ, between the experimental group and the control group (P< 0.05). The levels of IL-2, IL-6, TNF-α, IFN-γ, and IL-17A in the non-pregnant group with poor treatment effect in the experimental group were significantly higher than those in the pregnant group (P< 0.05). Based on cytokine data and reproductive outcomes of patients, a new prognostic prediction model for immune infertility was successfully constructed with the PyCaret library. The bagging quadratic discriminant analysis (Bagging QDA) model performed best on the training set, with a sensitivity of 72.73%, specificity of 81.25%, and accuracy of 72.41% on the test set. In summary, immune infertility is associated with Th1/Th2 cytokine abnormalities, while the Bagging QDA model has high accuracy in predicting the prognosis of immune infertility.

Key words： Infertility Immune Cytokine Machine learning

Received: 12 October 2023 Published: 16 January 2024

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Cite this article:

Jing YANG, Zi-xing ZHONG, Wan-mao NI. Prediction of Prognosis in Women with Infrtility due to Immune Causes Based on Cytokine Levels and Machine Learning. China Biotechnology, 2023, 43(12): 169-176.

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https://manu60.magtech.com.cn/biotech/10.13523/j.cb.2310050 OR https://manu60.magtech.com.cn/biotech/Y2023/V43/I12/169

Table 1 Comparison of cytokine between 96 women with immune infertility (experimental group) and 57 normal pregnant women (control group)

Table 2 Comparison of cytokine levels between 46 non-pregnant patients with immune infertility (non-pregnant group) and 50 successfully pregnant patients with immune infertility (pregnant group)

Fig.1 PyCaret multiple models comparison results

Fig.2 Performance indicators for optimization of QDA model parameters, 10 cross validation results

Fig.3 Performance indicators after integration of Bagging and QDA models, 10 cross-validation results

Fig.4 ROC curve and AUC values of Bagging QDA model in test set 0 means patients that the true outcomes were infertile (non-pregnant group) and 1 means patients that the true outcomes were fertile (pregnant group)

Fig.5 Confusion matrix of test set prediction results by Bagging QDA model 0 means patients that the true outcomes were infertile (non-pregnant group) and 1 means patients that the true outcomes were fertile (pregnant group)


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