Goyal Palak, Rani Rinkle, Singh Karamjeet
Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147001, India.
J Neural Transm (Vienna). 2025 Jan;132(1):67-93. doi: 10.1007/s00702-024-02830-x. Epub 2024 Sep 9.
Neurodegenerative diseases are group of debilitating and progressive disorders that primarily affect the structure and functions of nervous system, leading to gradual loss of neurons and subsequent decline in cognitive, and behavioral activities. The two frequent diseases affecting the world's significant population falling in the above category are Alzheimer's disease (AD) and Parkinson's disease (PD). These disorders substantially impact the quality of life and burden healthcare systems and society. The demographic characteristics, and machine learning approaches have now been employed to diagnose these illnesses; however, they possess accuracy limitations. Therefore, the authors have developed ranking-based ensemble approach based on the weighted strategy of deep learning classifiers. The whole modeling procedure of the proposed approach incorporates three phases. In phase I, preprocessing techniques are applied to clean the noise in datasets to make it standardized according to deep learning models as it significantly impacts their performance. In phase II, five deep learning models are selected for classification and calculation of prediction results. In phase III, a ranking-based ensemble approach is proposed to ensemble the results of the five models after calculating the ranks and weights of them. In addition, the Magnetic Resonance Imaging (MRI) datasets named Alzheimer's Disease Neuroimaging Initiative (ADNI) for AD classification and Parkinson's Progressive Marker Initiative (PPMI) for PD classification are selected to validate the proposed approach. Furthermore, the proposed method achieved the classification accuracy on AD- Cognitive Normals (CN) at 97.89%, AD- Mild Cognitive Impairment (MCI) at 99.33% and CN-MCI at 99.44% and on PD-CN at 99.22%, PD- Scans Without Evidence of Dopaminergic Effect (SWEDD) at 97.56% and CN-SWEDD at 98.22% respectively. Also, the multi-class classification shows the promising accuracy of 97.18% for AD and 97.85% for PD for the proposed framework. The findings of the study show that the proposed deep learning-based ensemble technique is competitive for AD and PD prediction in both multiclass and binary class classification. Furthermore, the proposed approach enhances generalization performance in diagnosing neurodegenerative diseases and performs better than existing approaches.
神经退行性疾病是一组使人衰弱且呈进行性发展的疾病，主要影响神经系统的结构和功能，导致神经元逐渐丧失，进而使认知和行为活动衰退。属于上述类别的、影响全球大量人口的两种常见疾病是阿尔茨海默病（AD）和帕金森病（PD）。这些疾病对生活质量产生重大影响，并给医疗保健系统和社会带来负担。目前已采用人口统计学特征和机器学习方法来诊断这些疾病；然而，它们存在准确性方面的局限性。因此，作者基于深度学习分类器的加权策略开发了基于排序的集成方法。所提出方法的整个建模过程包括三个阶段。在第一阶段，应用预处理技术清理数据集中的噪声，使其根据深度学习模型进行标准化，因为这会显著影响其性能。在第二阶段，选择五个深度学习模型进行分类并计算预测结果。在第三阶段，提出一种基于排序的集成方法，在计算五个模型的排名和权重后对它们的结果进行集成。此外，选择用于AD分类的名为阿尔茨海默病神经影像倡议（ADNI）的磁共振成像（MRI）数据集和用于PD分类的帕金森病进展标记倡议（PPMI）数据集来验证所提出的方法。此外，所提出的方法在AD-认知正常（CN）上的分类准确率为97.89%，在AD-轻度认知障碍（MCI）上为99.33%，在CN-MCI上为99.44%；在PD-CN上为99.22%，在PD-无多巴胺能效应证据扫描（SWEDD）上为97.56%，在CN-SWEDD上为98.22%。而且，对于所提出的框架，多类分类显示AD的准确率为97.18%，PD的准确率为97.85%，前景可观。该研究结果表明，所提出的基于深度学习的集成技术在多类和二类分类中对AD和PD预测具有竞争力。此外，所提出的方法在诊断神经退行性疾病时提高了泛化性能，并且比现有方法表现更好。
