Graduate Program in Applied Informatics, University of Fortaleza Av. Washington Soares, 1321 - Edson Queiroz - CEP, 60811-905, Fortaleza, CE, Brazil; Graduate Program on Teleinformatics Engineering / Federal University of Ceará, Fortaleza, Fortaleza/CE, Brazil.
Graduate Program on Teleinformatics Engineering / Federal University of Ceará, Fortaleza, Fortaleza/CE, Brazil.
Comput Biol Med. 2021 Apr;131:104260. doi: 10.1016/j.compbiomed.2021.104260. Epub 2021 Feb 10.
Parkinson's disease (PD) is a progressive neurodegenerative illness associated with motor skill disorders, affecting thousands of people, mainly elderly, worldwide. Since its symptoms are not clear and commonly confused with other diseases, providing early diagnosis is a challenging task for traditional methods. In this context, computer-aided assistance is an alternative method for a fast and automatic diagnosis, accelerating the treatment and alleviating an excessive effort from professionals. Moreover, the most recent studies proposing a solution to this problem lack in computational efficiency, prediction power, reliability among other factors. Therefore, this work proposes a Fuzzy Optimum Path Forest for automated PD identification, which is based on fuzzy logic and graph-based framework theory. Experiments consider a dataset composed of features extracted from hand-drawn images using Restricted Boltzmann Machines, and results are compared with baseline models such as Support Vector Machines, KNN, and the standard OPF classifier. Results show that the proposed model outperforms the baselines in most cases, suggesting the Fuzzy OPF as a viable alternative to deal with PD detection problems.
帕金森病（PD）是一种与运动技能障碍相关的进行性神经退行性疾病，影响着全球成千上万的人，主要是老年人。由于其症状不明显，常与其他疾病混淆，因此传统方法很难进行早期诊断。在这种情况下，计算机辅助是一种快速自动诊断的替代方法，可以加速治疗并减轻专业人员的过度负担。此外，最近提出的解决这个问题的研究在计算效率、预测能力、可靠性等方面存在不足。因此，这项工作提出了一种基于模糊逻辑和基于图的框架理论的模糊最优路径森林，用于自动 PD 识别。实验考虑了一个由使用受限玻尔兹曼机从手绘图像中提取的特征组成的数据集，并将结果与支持向量机、KNN 和标准 OPF 分类器等基线模型进行了比较。结果表明，在大多数情况下，所提出的模型优于基线模型，这表明模糊 OPF 是一种可行的替代方法，可以用于处理 PD 检测问题。
