Demir Bahar, Ayna Altuntaş Sinem, Kurt İlke, Ulukaya Sezer, Erdem Oğuzhan, Güler Sibel, Uzun Cem
Department of Computational Sciences, Trakya University, Edirne, 22030, Turkey.
Department of Biomedical Device Technology, Trakya University, Edirne, 22030, Turkey.
Neurol Sci. 2025 Jan;46(1):147-155. doi: 10.1007/s10072-024-07734-y. Epub 2024 Sep 11.
The development of modern Artificial Intelligence (AI) based models for the early diagnosis of Parkinson's disease (PD) has been gaining deep attention by researchers recently. In particular, the use of different types of datasets (voice, hand movements, gait, etc.) increases the variety of up-to-date models. Movement disorders and tremors are also among the most prominent symptoms of PD. The usage of drawings in the detection of PD can be a crucial decision-support approach that doctors can benefit from.
A dataset was created by asking 40 PD and 40 Healthy Controls (HC) to draw spirals with and without templates using a special tablet. The patient-healthy distinction was achieved by classifying drawings of individuals using Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) algorithms. Prior to classification, the data were normalized by applying the min-max normalization method. Moreover, Leave-One-Subject-Out (LOSO) Cross-Validation (CV) approach was utilized to eliminate possible overfitting scenarios. To further improve the performances of classifiers, Principal Component Analysis (PCA) dimension reduction technique were also applied to the raw data and the results were compared accordingly.
The highest accuracy among machine learning based classifiers was obtained as 90% with SVM classifier using non-template drawings with PCA application.
The model can be used as a pre-evaluation system in the clinic as a non-invasive method that also minimizes environmental and educational level differences by using simple hand gestures such as hand drawing, writing numbers, words, and syllables. As a result of our study, preliminary preparation has been made so that hand drawing analysis can be used as an auxiliary system that can save time for health professionals. We plan to work on more comprehensive data in the future.
近年来，基于现代人工智能（AI）的帕金森病（PD）早期诊断模型的开发受到了研究人员的广泛关注。特别是，使用不同类型的数据集（语音、手部动作、步态等）增加了最新模型的多样性。运动障碍和震颤也是PD最突出的症状之一。在PD检测中使用绘图可能是医生可以从中受益的关键决策支持方法。
通过让40名PD患者和40名健康对照（HC）使用特殊平板电脑在有模板和无模板的情况下绘制螺旋线来创建一个数据集。通过使用支持向量机（SVM）、随机森林（RF）和朴素贝叶斯（NB）算法对个体的绘图进行分类来实现患者与健康对照的区分。在分类之前，通过应用最小-最大归一化方法对数据进行归一化。此外，采用留一受试者出（LOSO）交叉验证（CV）方法来消除可能的过拟合情况。为了进一步提高分类器的性能，还对原始数据应用主成分分析（PCA）降维技术，并相应地比较结果。
在基于机器学习的分类器中，使用应用PCA的无模板绘图的SVM分类器获得了最高90%的准确率。
该模型可作为临床中的预评估系统，作为一种非侵入性方法，通过使用诸如手绘、写数字、单词和音节等简单手势，还可最大限度地减少环境和教育水平差异。作为我们研究的结果，已经进行了初步准备，以便手绘分析可以用作辅助系统，为卫生专业人员节省时间。我们计划在未来处理更全面的数据。
