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An Improved Approach for Prediction of Parkinson's Disease using Machine Learning Techniques

机译：一种利用机器学习技术预测帕金森病的改进方法

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Parkinson's disease (PD) is one of the major public health problems in the world. It is a well-known fact that around one million people suffer from Parkinson's disease in the United States whereas the number of people suffering from Parkinson's disease worldwide is around 5 millions. Thus, it is important to predict Parkinson's disease in early stages so that early plan for the necessary treatment can be made. People are mostly familiar with the motor symptoms of Parkinson's disease, however an increasing amount of research is being done to predict the Parkinson's disease from non-motor symptoms that precede the motor ones. If early and reliable prediction is possible then a patient can get a proper treatment at the right time. Non-motor symptoms considered are Rapid Eye Movement (REM) sleep Behaviour Disorder (RBD) and olfactory loss. Developing machine learning models that can help us in predicting the disease can play a vital role in early prediction. In this paper we extend a work which used the non-motor features such as RBD and olfactory loss. Along with this the extended work also uses important biomarkers. In this paper we try to model this classifier using different machine learning models that have not been used before. We developed automated diagnostic models using Multilayer Perceptron, BayesNet, Random Forest and Boosted Logistic Regression. It has been observed that Boosted Logistic Regression provides the best performance with an impressive accuracy of 97.159% and the area under the ROC curve was 98.9%. Thus, it is concluded that this models can be used for early prediction of Parkinson's disease.
机译：帕金森病（PD）是世界上主要的公共卫生问题之一。这是一个众所周知的事实，大约一百万人患有美国帕金森病的疾病，而全球帕金森病的人数约为500万。因此，重要的是在早期阶段预测帕金森病，因此可以进行必要的治疗的早期计划。人们大多熟悉帕金森病的运动症状，但是正在进行越来越多的研究，以预测在电机的非运动症状中的帕金森病。如果可能的预测是可能的，那么患者可以在正确的时间得到适当的治疗。认为非运动症状是快速眼动（REM）睡眠行为障碍（RBD）和嗅觉损失。开发机器学习模型，可以帮助我们预测疾病在早期预测中起着至关重要的作用。在本文中，我们扩展了一种使用诸如RBD和嗅觉损失等非电机功能的工作。除此之外，扩展工作也使用重要的生物标志物。在本文中，我们尝试使用之前未使用的不同机器学习模型来模拟此类分类器。我们开发了使用Multidayer Perceptron，Bayesnet，随机森林和提升Logistic回归的自动诊断模型。已经观察到，增强物流回归提供了最佳性能，令人印象深刻的精度为97.159％，ROC曲线下的面积为98.9％。因此，得出结论，该模型可用于帕金森病的早期预测。

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