Shams Alden Zahraa Noor Aldeen M, Ata Oguz
Faculty of Tourism Science, University of Kerbala, Kerbala, Iraq.
Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey.
PeerJ Comput Sci. 2024 Dec 4;10:e2338. doi: 10.7717/peerj-cs.2338. eCollection 2024.
Medical data analysis is an expanding area of study that holds the promise of transforming the healthcare landscape. The use of available data by researchers gives guidelines to improve health practitioners' decision-making capacity, thus enhancing patients' lives. The study looks at using deep learning techniques to predict the onset of osteoporosis from the NHANES 2017-2020 dataset that was preprocessed and arranged into SpineOsteo and FemurOsteo datasets. Two feature selection methods, namely mutual information (MI) and recursive feature elimination (RFE), were applied to sequential deep neural network models, convolutional neural network models, and recurrent neural network models. It can be concluded from the models that the mutual information method achieved higher accuracy than recursive feature elimination, and the MI feature selection CNN model showed better performance by showing 99.15% accuracy for the SpineOsteo dataset and 99.94% classification accuracy for the FemurOsteo dataset. Key findings of this study include family medical history, cases of fractures in patients and parental hip fractures, and regular use of medications like prednisone or cortisone. The research underscores the potential for deep learning in medical data processing, which eventually opens the way for enhanced models for diagnosis and prognosis based on non-image medical data. The implications of the study shall then be important for healthcare providers to be more informed in their decision-making processes for patients' outcomes.
医学数据分析是一个不断扩展的研究领域，有望改变医疗保健格局。研究人员对现有数据的使用为提高医疗从业者的决策能力提供了指导方针，从而改善患者生活。该研究着眼于使用深度学习技术，从经过预处理并整理成脊柱骨质疏松症（SpineOsteo）和股骨骨质疏松症（FemurOsteo）数据集的2017 - 2020年美国国家健康与营养检查调查（NHANES）数据集中预测骨质疏松症的发病情况。两种特征选择方法，即互信息（MI）和递归特征消除（RFE），被应用于序列深度神经网络模型、卷积神经网络模型和循环神经网络模型。从这些模型可以得出结论，互信息方法比递归特征消除取得了更高的准确率，并且MI特征选择的卷积神经网络模型表现出更好的性能，在脊柱骨质疏松症数据集上的准确率为99.15%，在股骨骨质疏松症数据集上的分类准确率为99.94%。这项研究的主要发现包括家族病史、患者骨折病例和父母髋部骨折，以及泼尼松或可的松等药物的常规使用。该研究强调了深度学习在医学数据处理中的潜力，最终为基于非图像医学数据的增强诊断和预后模型开辟了道路。该研究的意义对于医疗保健提供者在为患者的治疗结果进行决策时更具参考价值至关重要。
