摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-详细介绍了以人为中心的智能系统的数据。根据NewsRx编辑来自南北大学的消息，这项研究表明：“帕金森氏病(PD)是一种神经系统疾病，其特征是运动和非运动功能都被破坏。鉴于缺乏明确的诊断方法，找出其根源是非常重要的。”新闻编辑们引用了南北大学的一句话：“因此，表现出帕金森氏症症状的个人可以很容易地接受治疗和全面护理。为了解决这个问题，我们的研究旨在开发一种人工智能系统，能够检测帕金森氏症，并进一步评估影响其发展的主要因素。我们从著名的PPMI数据库中收集了12个不同的数据集，涵盖了各种Medica L评估，如运动能力、嗅觉、认知、睡眠模式和抑郁症状。随后，我们使用先进的SEAR CH技术对原始数据进行了改进，以适应我们的模型要求。此外，我们引入了一种新的标记方法，称为多数投票算法。在数据准备之后，我们进行了单模态和多模态分析。为了分析这两种方法，我们采用了五种不同的机器学习算法，特别是支持向量机(Linear)作为T OP执行者，在单模和多模分析中都达到了100%的准确率。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on human-centric intelli gent systems have been presented. According to news originating from North South University by NewsRx editors, the research stated, "Parkinson's disease (PD) is a neurological condition characterized by the disruption of both motor and non- motor functions. Given the absence of a definitive diagnostic method, it is cruc ial to uncover its root causes." The news editors obtained a quote from the research from North South University: "Consequently, individuals displaying symptoms of Parkinson's disease can promp tly receive treatment and comprehensive care. To address this, our study aims to develop an AI-powered system capable of detecting Parkinson's disease and subse quently evaluating the primary factors influencing its development. We collected 12 distinct datasets from the well-known PPMI database, covering various medica l assessments such as motor abilities, olfaction, cognition, sleep patterns, and depressive symptoms. Subsequently, we refined this raw data using advanced sear ch techniques to tailor it to our model's requirements. Moreover, we introduced a novel labeling approach known as the majority voting algorithm. Following data preparation, we conducted Single and Multi-Modality analyses, focusing on singl e-treatment approaches and integrating multiple treatments for a comprehensive t herapeutic strategy. To analyze these both, we employed five distinct Machine Le arning algorithms. Notably, the Support Vector Machine (linear) emerged as the t op performer, reaching an accuracy of 100% in both single and mult imodality analysis."
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