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    Application of Big Data Technology to Assessments of Female Ovarian Reserve Dysfunction by Xia Ji'An, Ma YunFei, Wu YiYun, Zhao YouLin, Ni HaoRang, Liu XinYan

    Published 2025-01-01
    “…Patients were divided into normal ovarian reserve function (n = 68), early-stage DOR (n = 66), mid-stage DOR (n = 12), and late-stage DOR (n = 16) groups. Hadoop and Spark frameworks were used to build a big data platform, and the MLlib parallel machine learning library was used to implement three multivariate classification models—multilayer perceptron, one-vs-rest, and random forest classifiers—to classify and analyse the ovarian reserve function dataset and evaluate the platform’s performance. …”
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