Department of Computer Engineering, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, Pune, 413133, Maharashtra, India.
M.D. Research, Intervention Treatment Institute, Houston, TX, USA.
BMC Med Inform Decis Mak. 2024 Apr 5;24(1):92. doi: 10.1186/s12911-024-02495-2.
Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.
从数字孪生技术和元宇宙的融合中出现的消费者健康 (MCH) 正在经历一场变革。生物信息学与医疗保健大数据的融合带来了利用全面医疗数据的疾病预测模型的新时代，甚至可以在症状出现之前预测疾病。在这个模型中，深度神经网络脱颖而出，因为通过增加网络深度和使用梯度下降进行权重更改，它们显著提高了准确性。然而，传统方法也面临着自己的一系列挑战，包括梯度不稳定和训练缓慢的问题。在这种情况下，广义学习系统 (BLS) 是一个不错的选择。它解决了梯度下降的问题，并允许您通过增量学习快速重建模型。BLS 的一个问题是，它难以从复杂的医疗数据中提取复杂的特征。这使得它在广泛的医疗保健情况下用处不大。针对这些挑战，我们引入了 DAE-BLS，这是一种新颖的混合模型，它将降噪自动编码器 (DAE) 的降噪功能与 BLS 的效率结合在一起。这种混合方法在稳健的特征提取方面表现出色，尤其是在医疗数据复杂多样的世界中。使用不同数据集进行验证的结果令人印象深刻，准确率高达 98.50%。DAE-BLS 通过增量学习快速适应的能力为准确和灵活的疾病预测提供了巨大的潜力，尤其是在当今复杂和动态的医疗保健环境中。
