Parkinson’s Disease Predictor via Voice Analysis

Alankar Uniyal, Ayush Patel, Ritesh Dhanare
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Abstract

with the increasing integration of automobiles in our daily lives, the number of four-wheelers on the road has seen a substantial jump in the tally. Furthermore, the number of drivers has also increased. Moreover, people nowadays have a slightly higher chance to opt for a taxi for daily commute. With this statistic, a coinciding fact that the number of Parkinson’s Disease cases have also increased cannot be overlooked. Also, the advancement in the technology of machine learning has enabled us to accurately detect Parkinson’s Disease with unorthodox testing techniques like voice analysis. With these things in mind, we have attempted to use machine learning to predict whether a person has Parkinson’s disease or not using their voice samples whilst designing the model to assign higher weights to features that help accurately classify the voice sample. For Example, pitch being a critical factor to determine if the person is showing an excited emotional state. Once the model reaches the desired generalization ability, it can be integrated into the recruiting process of organizations like uber.
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通过语音分析预测帕金森病
随着汽车日益融入我们的日常生活,道路上的四轮车数量大幅增加。此外,司机的数量也有所增加。此外,现在人们选择出租车上下班的可能性也有所增加。有了这个统计数据,一个巧合的事实是,帕金森氏症病例的数量也在增加,这一点不容忽视。此外,机器学习技术的进步使我们能够通过语音分析等非常规测试技术准确检测帕金森氏症。考虑到这些,我们尝试使用机器学习来预测一个人是否患有帕金森病,同时设计模型,为有助于准确分类语音样本的特征分配更高的权重。例如,音调是决定一个人是否表现出兴奋情绪的关键因素。一旦模型达到预期的泛化能力,就可以整合到uber等组织的招聘过程中。
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