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Clinical Prediction of Female Infertility Through Advanced Machine Learning Techniques

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  • Fida Muhammad Khan

    (Department of Computer Science, Qurtuba University of Science & Information, Technology, Peshawar, Pakistan)

Abstract

Infertility in females implies failure by such women to conceive even after having at least one year of intercourse without using any contraceptives. Infertility can be caused by a variety of factors, including ovulation problems, blocked fallopian tubes, hormone imbalances, and abnormalities of the uterus and so on. Infertility can negatively impact people's emotional, psychological, and social well-being. Our proposed study utilizes advanced machine learning techniques to present an innovative and novel method for predicting female infertility. We analyzed a dataset with medical attributes related to reproductive health using logistic regression, Naive Bayes, Support Vector Machines (SVM), and Random Forest algorithms. The Random Forest algorithm achieved an outstanding accuracy rate of 93%, with its exceptional capabilities. The findings show that in the future, this model can be used to diagnose infertility early and provide personalized treatment recommendations. The results of this study have practical implications for reproductive healthcare, as well as providing much-needed support to infertile couples and individuals.

Suggested Citation

  • Fida Muhammad Khan, 2024. "Clinical Prediction of Female Infertility Through Advanced Machine Learning Techniques," International Journal of Innovations in Science & Technology, 50sea, vol. 6(2), pages 900-917, June.
    • Handle: RePEc:abq:ijist1:v:6:y:2024:i:2:p:900-917
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    File URL: https://journal.50sea.com/index.php/IJIST/article/view/913/1461
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    References listed on IDEAS

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    3. Cheng-Wei Wang & Chao-Yang Kuo & Chi-Huang Chen & Yu-Hui Hsieh & Emily Chia-Yu Su, 2022. "Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-17, June.
    4. Christian Kauten & Ashish Gupta & Xiao Qin & Glenn Richey, 2022. "Predicting Blood Donors Using Machine Learning Techniques," Information Systems Frontiers, Springer, vol. 24(5), pages 1547-1562, October.
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