The problem of cancer risk analysis is of great importance to health-service providers and medical researchers. In this study, we propose a novel Artificial Neural Network (ANN) algorithm based on the probabilistic framework, which aims to investigate patient patterns associated with their disease development. Compared to the traditional ANN where input features are directly extracted from raw data, the proposed probabilistic ANN manipulates original inputs according to their probability distribution. More precisely, the Naïve Bayes and Markov chain models are used to approximate the posterior distribution of the raw inputs, which provides a useful estimation of subsequent disease development. Later, this distribution information is further leveraged as additional input to train ANN. Additionally, to reduce the training cost and to boost the generalization capability, a sparse training strategy is also introduced. Experimentally, one of the largest cancer-related datasets is employed in this study. Compared to state-of-the-art methods, the proposed algorithm achieves a much better outcome, in terms of the prediction accuracy of subsequent disease development. The result also reveals the potential impact of patients' disease sequence on their future risk management.

中文翻译：

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基于改进概率神经网络的癌症风险分析

癌症风险分析问题对卫生服务提供者和医学研究人员来说非常重要。在这项研究中，我们提出了一种基于概率框架的新型人工神经网络 (ANN) 算法，旨在研究与其疾病发展相关的患者模式。与直接从原始数据中提取输入特征的传统 ANN 相比，所提出的概率 ANN 根据原始输入的概率分布对其进行操作。更准确地说，朴素贝叶斯和马尔可夫链模型用于近似原始输入的后验分布，这提供了对后续疾病发展的有用估计。后来，这个分布信息被进一步用作训练 ANN 的额外输入。此外，为了降低训练成本并提高泛化能力，还引入了稀疏训练策略。在实验上，本研究采用了最大的癌症相关数据集之一。与最先进的方法相比，所提出的算法在后续疾病发展的预测准确性方面取得了更好的结果。结果还揭示了患者疾病序列对其未来风险管理的潜在影响。