论文标题
通过挑战帕金森氏病的挑战，使用语音记录来监测帕金森氏病进展，对制动数据的机器学习模型的比较研究
A Comparative Study of Machine Learning Models for Tabular Data Through Challenge of Monitoring Parkinson's Disease Progression Using Voice Recordings
论文作者
论文摘要
帕金森氏病患者必须经常受到医生的监测，以观察该疾病的进展情况，并可能调整治疗计划以减轻症状。通过患者在自己家中捕获的声音记录来监测疾病的进展，可以使过程更快，压力更低。我们使用了6个月内有42人患有早期帕金森氏病的人的语音录音数据集，我们应用了多个机器学习技术，以找到语音录音与患者的运动UPDRS分数之间的相关性。我们使用多种回归和分类技术解决了这个问题。本文的大部分内容都致力于使用回归技术将语音数据映射到电动机上的分数，以便为未知实例获得更精确的价值。通过对变异机学习方法的比较研究，我们意识到了一些旧的机器学习方法，例如树木的表现优于尖端的尖端深度学习模型。
People with Parkinson's disease must be regularly monitored by their physician to observe how the disease is progressing and potentially adjust treatment plans to mitigate the symptoms. Monitoring the progression of the disease through a voice recording captured by the patient at their own home can make the process faster and less stressful. Using a dataset of voice recordings of 42 people with early-stage Parkinson's disease over a time span of 6 months, we applied multiple machine learning techniques to find a correlation between the voice recording and the patient's motor UPDRS score. We approached this problem using a multitude of both regression and classification techniques. Much of this paper is dedicated to mapping the voice data to motor UPDRS scores using regression techniques in order to obtain a more precise value for unknown instances. Through this comparative study of variant machine learning methods, we realized some old machine learning methods like trees outperform cutting edge deep learning models on numerous tabular datasets.
