论文标题
PCA-RF：基于随机森林分类的高效帕金森氏病预测模型
PCA-RF: An Efficient Parkinson's Disease Prediction Model based on Random Forest Classification
论文作者
论文摘要
在现代人口过多的时代，疾病预测是早期诊断各种疾病的关键步骤。随着各种机器学习算法的发展，预测变得非常容易。但是，为给定数据集的复杂机器学习技术的选择和选择极大地影响了模型的准确性。全球存在大量数据集，但由于其非结构化格式，因此没有有效的使用。因此，许多不同的技术可用于提取对现实世界有用的东西。因此，准确性成为评估模型的主要指标。在本文中，提出了一种疾病预测方法，该方法在帕金森氏病上实现了随机的森林分类器。我们将该模型的准确性与主成分分析（PCA）应用的人工神经网络（ANN）模型进行了比较，并捕获了可见的差异。该模型的明显精度高达90％。
In this modern era of overpopulation disease prediction is a crucial step in diagnosing various diseases at an early stage. With the advancement of various machine learning algorithms, the prediction has become quite easy. However, the complex and the selection of an optimal machine learning technique for the given dataset greatly affects the accuracy of the model. A large amount of datasets exists globally but there is no effective use of it due to its unstructured format. Hence, a lot of different techniques are available to extract something useful for the real world to implement. Therefore, accuracy becomes a major metric in evaluating the model. In this paper, a disease prediction approach is proposed that implements a random forest classifier on Parkinson's disease. We compared the accuracy of this model with the Principal Component Analysis (PCA) applied Artificial Neural Network (ANN) model and captured a visible difference. The model secured a significant accuracy of up to 90%.
