论文标题
多囊卵巢综合征预后的对比度的判别分析
Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication
论文作者
论文摘要
已经制定了许多预后方法，用于早期检测多囊卵巢综合征，也称为PCOS，使用机器学习。 PCOS是一个二进制分类问题。减少维度的方法会在更大程度上影响机器学习的性能，并使用监督维度降低方法可以使我们有了新的优势来解决此问题。在本文中，我们以不同的维度和二进制分类以及指标的线性和二次形式进行不同维度的判别分析。与许多常用的分类算法相比，我们能够实现良好的准确性和更少的变化，而训练精度达到97.37％，使用二次判别分析的测试精度为95.92％。论文还提供了具有可视化的数据分析，以深入了解问题。
A lot of prognostication methodologies have been formulated for early detection of Polycystic Ovary Syndrome also known as PCOS using Machine Learning. PCOS is a binary classification problem. Dimensionality Reduction methods impact the performance of Machine Learning to a greater extent and using a Supervised Dimensionality Reduction method can give us a new edge to tackle this problem. In this paper we present Discriminant Analysis in different dimensions with Linear and Quadratic form for binary classification along with metrics. We were able to achieve good accuracy and less variation with Discriminant Analysis as compared to many commonly used classification algorithms with training accuracy reaching 97.37% and testing accuracy of 95.92% using Quadratic Discriminant Analysis. Paper also gives the analysis of data with visualizations for deeper understanding of problem.
