Department of Computer Engineering, Aligarh Muslim University, Aligarh, India.
Department of Business Administration, College of Business Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Brain Behav. 2024 Aug;14(8):e3519. doi: 10.1002/brb3.3519.
Neurological disorders pose a significant health challenge, and their early detection is critical for effective treatment planning and prognosis. Traditional classification of neural disorders based on causes, symptoms, developmental stage, severity, and nervous system effects has limitations. Leveraging artificial intelligence (AI) and machine learning (ML) for pattern recognition provides a potent solution to address these challenges. Therefore, this study focuses on proposing an innovative approach-the Aggregated Pattern Classification Method (APCM)-for precise identification of neural disorder stages.
The APCM was introduced to address prevalent issues in neural disorder detection, such as overfitting, robustness, and interoperability. This method utilizes aggregative patterns and classification learning functions to mitigate these challenges and enhance overall recognition accuracy, even in imbalanced data. The analysis involves neural images using observations from healthy individuals as a reference. Action response patterns from diverse inputs are mapped to identify similar features, establishing the disorder ratio. The stages are correlated based on available responses and associated neural data, with a preference for classification learning. This classification necessitates image and labeled data to prevent additional flaws in pattern recognition. Recognition and classification occur through multiple iterations, incorporating similar and diverse neural features. The learning process is finely tuned for minute classifications using labeled and unlabeled input data.
The proposed APCM demonstrates notable achievements, with high pattern recognition (15.03%) and controlled classification errors (CEs) (10.61% less). The method effectively addresses overfitting, robustness, and interoperability issues, showcasing its potential as a powerful tool for detecting neural disorders at different stages. The ability to handle imbalanced data contributes to the overall success of the algorithm.
The APCM emerges as a promising and effective approach for identifying precise neural disorder stages. By leveraging AI and ML, the method successfully resolves key challenges in pattern recognition. The high pattern recognition and reduced CEs underscore the method's potential for clinical applications. However, it is essential to acknowledge the reliance on high-quality neural image data, which may limit the generalizability of the approach. The proposed method allows future research to refine further and enhance its interpretability, providing valuable insights into neural disorder progression and underlying biological mechanisms.
神经系统疾病是一个重大的健康挑战，早期发现对于有效的治疗计划和预后至关重要。传统的基于病因、症状、发育阶段、严重程度和神经系统影响的神经疾病分类方法存在局限性。利用人工智能（AI）和机器学习（ML）进行模式识别为解决这些挑战提供了一种强大的解决方案。因此，本研究专注于提出一种创新方法——聚合模式分类方法（APCM）——用于精确识别神经障碍阶段。
引入 APCM 方法是为了解决神经疾病检测中常见的问题，如过拟合、稳健性和互操作性。该方法利用聚合模式和分类学习功能来减轻这些挑战，提高整体识别准确性，即使在不平衡数据中也是如此。分析涉及使用健康个体的观察结果来对神经图像进行分析。从不同输入映射动作响应模式以识别相似特征，建立疾病比例。根据可用的反应和相关的神经数据对阶段进行关联，偏好分类学习。这种分类需要图像和标记数据，以防止模式识别中的额外缺陷。通过多次迭代进行识别和分类，合并相似和不同的神经特征。使用标记和未标记的输入数据对学习过程进行微调，以进行细微分类。
提出的 APCM 方法取得了显著的成果，具有较高的模式识别率（15.03%）和较低的分类错误率（低 10.61%）。该方法有效地解决了过拟合、稳健性和互操作性问题，展示了其作为一种强大的工具，可用于在不同阶段检测神经障碍的潜力。处理不平衡数据的能力是算法整体成功的关键。
APCM 方法是一种用于识别精确神经障碍阶段的有前途和有效的方法。通过利用 AI 和 ML，该方法成功解决了模式识别中的关键挑战。高模式识别率和低分类错误率突显了该方法在临床应用中的潜力。然而，需要注意的是，该方法依赖于高质量的神经图像数据，这可能限制了该方法的通用性。该方法允许未来的研究进一步改进和增强其可解释性，为神经障碍进展和潜在的生物学机制提供有价值的见解。
