RADIANTYOU: Personalized PCOS Prediction Partner

International Journal of Advanced Research in Science, Communication and Technology Pub Date : 2024-05-12 DOI:10.48175/ijarsct-18210

DV Swetha Ramana, Kahakashan, Harshula M, Jahnavi S, Jhansi Devi M

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Abstract

The abstract outlines a study aimed at addressing the challenge of detecting polycystic ovary syndrome (PCOS) in women, particularly in Asia where a significant portion of cases go undetected. PCOS is a complex hormonal disorder affecting reproductive health, characterized by irregular menstrual cycles, excessive androgen levels, and the presence of multiple cysts on the ovaries. The researchers employed machine learning techniques to develop a predictive model for early detection of PCOS. This approach leverages data on various physiological markers such as prolactin levels, blood pressure, thyroid-stimulating hormone (TSH), and pregnancy status. These factors are known to be associated with PCOS and can potentially serve as indicators for its presence. The abstract highlights the effectiveness of Random Forest, a machine learning algorithm, in accurately predicting PCOS with minimal computational time. This implies that the model developed by the researchers can reliably identify individuals at risk of PCOS, allowing for early intervention and management

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RADIANTYOU：个性化多囊卵巢综合症预测合作伙伴

摘要概述了一项研究，该研究旨在应对检测女性多囊卵巢综合症（PCOS）的挑战，尤其是在亚洲，因为亚洲有相当一部分病例未被检测出来。多囊卵巢综合征是一种影响生殖健康的复杂荷尔蒙紊乱，其特点是月经周期不规律、雄激素水平过高以及卵巢上存在多个囊肿。研究人员采用机器学习技术开发了一个预测模型，用于早期检测多囊卵巢综合症。这种方法利用了催乳素水平、血压、促甲状腺激素（TSH）和怀孕状态等各种生理指标的数据。众所周知，这些因素与多囊卵巢综合症有关，可以作为多囊卵巢综合症的潜在指标。论文摘要强调了随机森林（一种机器学习算法）在准确预测多囊卵巢综合症方面的有效性，而且只需极少的计算时间。这意味着研究人员开发的模型可以可靠地识别有多囊卵巢综合症风险的个体，从而进行早期干预和管理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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International Journal of Advanced Research in Science, Communication and Technology

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