背景:神经系统疾病对健康构成重大挑战,并且它们的早期发现对于有效的治疗计划和预后至关重要。传统的基于病因的神经疾病分类,症状,发育阶段,严重程度,和神经系统的影响有局限性。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有效的解决方案。因此,这项研究的重点是提出一种创新的方法-聚合模式分类方法(APCM)-用于精确识别神经障碍阶段。
方法:引入APCM是为了解决神经障碍检测中的普遍问题,比如过拟合,鲁棒性,和互操作性。该方法利用聚合模式和分类学习功能来缓解这些挑战并提高整体识别精度。即使在不平衡的数据中。该分析涉及使用健康个体的观察结果作为参考的神经图像。来自不同输入的动作反应模式被映射以识别相似的特征,建立无序比率。这些阶段基于可用的响应和相关的神经数据进行关联,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其他缺陷。识别和分类通过多次迭代发生,融合了相似和多样的神经特征。使用标记和未标记的输入数据对学习过程进行微调,以进行微小分类。
结果:拟议的APCM展示了显著的成就,具有高模式识别(15.03%)和受控分类误差(CEs)(减少10.61%)。该方法有效地解决了过拟合,鲁棒性,和互操作性问题,展示了其作为检测不同阶段神经疾病的强大工具的潜力。处理不平衡数据的能力有助于算法的整体成功。
结论:APCM是一种有前途且有效的方法,用于确定精确的神经障碍阶段。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。高模式识别和降低CEs强调了该方法的临床应用潜力。然而,必须承认对高质量神经图像数据的依赖,这可能会限制该方法的普遍性。所提出的方法允许未来的研究进一步完善并增强其可解释性,提供有关神经疾病进展和潜在生物学机制的有价值的见解。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.