Machine learning (ML) models have become capable of making critical decisions on our behalf. Nevertheless, due to complexity of these models, interpreting their decisions can be challenging, and humans cannot always control them. This paper provides explanations of decisions made by ML models in diagnosing four types of posterior fossa tumors: medulloblastoma, ependymoma, pilocytic astrocytoma, and brainstem glioma. The proposed methodology involves data analysis using kernel density estimations with Gaussian distributions to examine individual MRI features, conducting an analysis on the relationships between these features, and performing a comprehensive analysis of ML model behavior. This approach offers a simple yet informative and reliable means of identifying and validating distinguishable MRI features for the diagnosis of pediatric brain tumors. By presenting a comprehensive analysis of the responses of the four pediatric tumor types to each other and to ML models in a single source, this study aims to bridge the knowledge gap in the existing literature concerning the relationship between ML and medical outcomes. The results highlight that employing a simplistic approach in the absence of very large datasets leads to significantly more pronounced and explainable outcomes, as expected. Additionally, the study also demonstrates that the pre-analysis results consistently align with the outputs of the ML models and the clinical findings reported in the existing literature.
机器学习模型已能代我们做出关键决策。然而，由于这些模型的复杂性，解释其决策过程可能具有挑战性，人类无法始终对其进行有效控制。本文针对机器学习模型在诊断四种后颅窝肿瘤（髓母细胞瘤、室管膜瘤、毛细胞星形细胞瘤和脑干胶质瘤）中的决策机制进行解释。所提出的方法包括：采用高斯分布核密度估计对个体MRI特征进行数据分析，探究这些特征间的关联性，并对机器学习模型的行为表现展开综合分析。该方法为识别和验证儿童脑肿瘤诊断中具有区分度的MRI特征提供了一种简洁高效且可靠的途径。通过在同一框架内系统分析四种儿童肿瘤类型彼此之间及其与机器学习模型的响应关系，本研究旨在填补现有文献中关于机器学习与医疗结果关联性的认知空白。研究结果强调，正如预期那样，在缺乏超大规模数据集的情况下采用简化方法，能够获得更为显著且可解释的结果。此外，研究还表明，预处理分析结果与机器学习模型的输出以及现有文献报道的临床发现始终保持一致。
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