AI 中文总结
研究利用机器学习方法结合独特特征选择算法预测多囊卵巢综合征，介绍结合特征工程和机器学习的数据驱动方法，经多种特征选择方法训练模型，结果显示经随机森林特征重要性和最高相关性选择十个特征的AdaBoost测试准确率最高。
AI 中文摘要
多囊卵巢综合征（PCOS）是育龄女性中普遍存在的激素问题。患有PCOS的女性可能不排卵，雄激素水平高且卵巢有许多小囊肿，会导致月经不规律等问题。传统检测方法包括临床评估等，但体检成本高。如今数据驱动技术推动疾病预测发展。本文旨在利用机器学习方法结合独特特征选择算法预测PCOS。介绍了结合特征工程和机器学习的数据驱动方法，考虑了多种特征选择方法训练模型，结果表明经随机森林特征重要性和最高相关性选择十个特征的AdaBoost测试准确率最高。
英文摘要
Polycystic Ovarian Syndrome (PCOS) is a widespread hormone problem for women of childbearing age. Women with PCOS may not ovulate; they might have high levels of androgens and have many small cysts on the ovaries. It can cause missed or irregular menstrual periods, excess hair growth, acne, infertility, and weight gain. Machine Learning (ML) can effectively diagnose this disease at an earlier stage as tons of medical data are available now. Traditional approaches to detect PCOS encompass a combination of clinical evaluation, medical history assessment, physical examination, and laboratory tests. These approaches aim to identify the characteristic symptoms and hormonal imbalances associated with PCOS. Physical examination requires good resources and costs time and money. In recent times, data-driven techniques have substantially advanced disease prediction within the medical field. We aim to utilize ML approaches, incorporating unique feature selection algorithms, to predict PCOS. This paper introduces a data-driven approach to PCOS diagnosis, combining Feature Engineering and ML. Several feature selection approaches have been considered to select sets of features for training the ML model, including CatBoost, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), AdaBoost, Random Forest (RF). Results demonstrate that AdaBoost, with ten features selected by RF Feature Importance and Highest Correlation (HC), provides the highest test accuracy.
CommentsPublished in 2023 5th International Conference on Sustainable Technologies for Industry 5.0 (STI), December 2023, Dhaka, Bangladesh
DOI：10.1109/STI59863.2023.10465079
