基于环境和生活方式因素的精液质量预测的自我监督体系

Ejay Nsugbe
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引用次数: 1

摘要

男性生育率似乎在下降,因此需要更有效和更容易获得的评估方法。人工智能方法通过基于问卷的信息源,包括从医学文献中选择的影响精液质量的因素,有效地预测精液质量。先前的工作已经看到了监督学习在预测精液质量方面的应用,但由于监督学习依赖于数据类标签的提供,可以说它依赖于外部智能干预,这可以转化为实际环境中的进一步成本和资源。相比之下,无监督学习方法根据目标函数将数据划分为簇和组,不依赖于提供类标签,并且可以允许预测平台的全自动流程。在本文中,我们应用了三种不同模型架构的无监督学习模型,即高斯混合模型(GMM), K-means和谱聚类(SC),以及低维嵌入方法,包括稀疏自编码器(SAE),主成分分析(PCA)和鲁棒PCA。SAE与SC算法相结合的结果最好,这可能是由于其非特异性和任意的聚类形状假设。现在,进一步的工作将涉及探索具有与SC相似框架的类似无监督学习算法,以研究以最大精度学习各种聚类的程度。
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Toward a Self-Supervised Architecture for Semen Quality Prediction Using Environmental and Lifestyle Factors
Male fertility has been seen to be declining, prompting for more effective and accessible means of its assessment. Artificial intelligence methods have been effective toward predicting semen quality through a questionnaire-based information source comprising a selection of factors from the medical literature which have been seen to influence semen quality. Prior work has seen the application of supervised learning toward the prediction of semen quality, but since supervised learning hinges on the provision of data class labels it can be said to depend on an external intelligence intervention, which can translate toward further costs and resources in practical settings. In contrast, unsupervised learning methods partition data into clusters and groups based on an objective function and do not rely on the provision of class labels and can allow for a fully automated flow of a prediction platform. In this paper, we apply three unsupervised learning models with different model architectures, namely Gaussian mixture model (GMM), K-means, and spectral clustering (SC), alongside low dimensional embedding methods which include sparse autoencoder (SAE), principal component analysis (PCA), and robust PCA. The best results were obtained with a combination of the SAE and the SC algorithm, which was likely due to its nonspecific and arbitrary cluster shape assumption. Further work would now involve the exploration of similar unsupervised learning algorithms with a similar framework to the SC to investigate the extent to which various clusters can be learned with maximal accuracy.
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