Neurodegenerative disorder such as Parkinson’s disease (PD) is among the severe health problems in our aging society. It is a neural disorder that affects people socially as well as economically. It occurs due to the failure of the brain’s dopamine-producing cells to produce enough dopamine to enable the motor movement of the body. This disease primarily affects vision, speech, movement problems, and excretion activity, followed by depression, nervousness, sleeping problems, and panic attacks. The onset of Parkinson’s disease is diagnosed with the help of speech disorders, which are the earliest symptoms of it. The essential goal of this paper is to build up a viable clinical decision-making system that helps the doctor in diagnosing the PD influenced patients. In this paper, a specific framework based on grid search optimization is proposed to develop an optimized deep learning Model to predict the early onset of Parkinson’s disease whereby multiple hyperparameters are to be set and tuned for evaluation of the deep learning model. The grid search optimization consists of three main stages, i.e., the optimization of the deep learning model topology, the hyperparameters, and its performance. An evaluation of the proposed approach is done on the speech samples of PD patients and healthy individuals. The results of the approach proposed are finally analyzed, which shows that the fine-tuning of the deep learning model parameters result in the overall test accuracy of 89.23% and the average classification accuracy of 91.69%.

中文翻译：

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深度学习模型的超参数优化预测帕金森氏病

帕金森氏病（PD）等神经退行性疾病是我们老龄化社会中严重的健康问题之一。这是一种神经疾病，会在社会和经济上影响人们。发生这种情况的原因是大脑的多巴胺产生细胞无法产生足够的多巴胺以使人体运动。该疾病主要影响视力，言语，运动问题和排泄活动，其次是抑郁，神经质，睡眠问题和惊恐发作。帕金森氏病的发作是通过言语障碍诊断的，言语障碍是最早的症状。本文的基本目标是建立一个可行的临床决策系统，以帮助医生诊断受PD影响的患者。在本文中，提出了一个基于网格搜索优化的特定框架来开发优化的深度学习模型，以预测帕金森氏病的早期发作，从而可以设置和调整多个超参数以评估深度学习模型。网格搜索优化包括三个主要阶段，即深度学习模型拓扑，超参数及其性能的优化。在PD患者和健康个体的语音样本上对提出的方法进行了评估。最后对提出的方法的结果进行了分析，结果表明，深度学习模型参数的微调导致整体测试准确度为89.23％，平均分类准确度为91.69％。