SLB - SMOTE logistic blending hybrid machine learning model for chronic polycystic ovary syndrome prediction with correlated feature selection.

Informatics for health & social care Pub Date : 2024-10-01 Epub Date: 2024-10-27 DOI:10.1080/17538157.2024.2405868

S Vairachilai, Devarakonda Anuhya, Anjeleen Tirkey, S P Raja

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引用次数: 0

Abstract

Objective: In this study, we aimed to develop a machine learning (ML) model for predicting Polycystic Ovary Syndrome (PCOS) based on demographic, clinical, and biochemical parameters.

Methodology: We collected data from Kaggle, which included information on age, body mass index, menstrual cycle length, follicle-stimulating hormone, hair growth, and more. Using this data, we trained several traditional ML and ensemble algorithms to predict PCOS.

Results: Among the traditional ML algorithms, Logistic Regression emerged as the best, boasting the highest accuracy of 0.91 and an AUC of 0.90. In ensemble algorithms, the Blending algorithm outperformed other ensemble methods, also achieving an accuracy of 0.91 and an AUC of 0.90, with a balanced precision and recall of 0.88.

Significance of the research: These results establish Logistic Regression and the Blending algorithm as optimal choices for accurate and reliable PCOS prediction, demonstrating strong discriminative power and the ability to correctly classify PCOS cases.

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SLB - 利用相关特征选择预测慢性多囊卵巢综合征的 SMOTE 逻辑混合混合机器学习模型。

目的：本研究旨在开发一种机器学习（ML）模型，根据人口统计学、临床和生化参数预测多囊卵巢综合征（PCOS）：在这项研究中，我们旨在开发一种机器学习（ML）模型，用于根据人口、临床和生化参数预测多囊卵巢综合症（PCOS）：我们从 Kaggle 收集了数据，其中包括年龄、体重指数、月经周期长度、卵泡刺激素、毛发生长等信息。利用这些数据，我们训练了几种传统的 ML 算法和集合算法来预测多囊卵巢综合症：在传统的 ML 算法中，逻辑回归（Logistic Regression）是最好的，准确率最高，达到 0.91，AUC 为 0.90。在集合算法中，混合算法的表现优于其他集合方法，准确率也达到了 0.91，AUC 为 0.90，精确度和召回率均为 0.88：这些结果表明，Logistic 回归和混合算法是准确可靠地预测多囊卵巢综合症的最佳选择，具有很强的判别能力和正确分类多囊卵巢综合症病例的能力。

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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Informatics for health & social care

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