论文标题
多囊卵巢综合征诊断预测的不同增强合奏学习方法的简洁分化
Succinct Differentiation of Disparate Boosting Ensemble Learning Methods for Prognostication of Polycystic Ovary Syndrome Diagnosis
论文作者
论文摘要
目前，使用临床数据利用机器学习技术来预测医疗问题是目前最重要的现实世界挑战之一。考虑到多囊卵巢综合征的医学问题也称为PCOS是15岁至49岁的女性的一个新兴问题。通过使用各种增强合奏方法诊断这种疾病是我们在本文中提出的。我们在本文中提出了一些详细的和汇总的区分，它们各自的性能指标强调了数据中隐藏的异常情况及其对结果的影响，这是我们在本文中提出的。本文使用了诸如混淆矩阵，精度，召回，F1分数，FPR，ROC曲线和AUC之类的指标。
Prognostication of medical problems using the clinical data by leveraging the Machine Learning techniques with stellar precision is one of the most important real world challenges at the present time. Considering the medical problem of Polycystic Ovary Syndrome also known as PCOS is an emerging problem in women aged from 15 to 49. Diagnosing this disorder by using various Boosting Ensemble Methods is something we have presented in this paper. A detailed and compendious differentiation between Adaptive Boost, Gradient Boosting Machine, XGBoost and CatBoost with their respective performance metrics highlighting the hidden anomalies in the data and its effects on the result is something we have presented in this paper. Metrics like Confusion Matrix, Precision, Recall, F1 Score, FPR, RoC Curve and AUC have been used in this paper.
