Alzakari Sarah A, Alruwais Nuha, Sorour Shaymaa, Ebad Shouki A, Hassan Elnour Asma Abbas, Sayed Ahmed
Department of Computer Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Department of Computer Science and Engineering, King Saud University, Riyadh, Saudi Arabia.
PeerJ Comput Sci. 2024 Nov 26;10:e2464. doi: 10.7717/peerj-cs.2464. eCollection 2024.
Big data analytics for clinical decision-making has been proposed for various clinical sectors because clinical decisions are more evidence-based and promising. Healthcare data is so vast and readily available that big data analytics has completely transformed this sector and opened up many new prospects. The smart sensor-based big data analysis recommendation system has significant privacy and security concerns when using sensor medical images for suggestions and monitoring. The danger of security breaches and unauthorized access, which might lead to identity theft and privacy violations, increases when sending and storing sensitive medical data on the cloud. Our effort will improve patient care and well-being by creating an anomaly detection system based on machine learning specifically for medical images and providing timely treatments and notifications. Current anomaly detection methods in healthcare systems, such as artificial intelligence and big data analytics-intracerebral hemorrhage (AIBDA-ICH) and parallel conformer neural network (PCNN), face several challenges, including high resource consumption, inefficient feature selection, and an inability to handle temporal data effectively for real-time monitoring. Techniques like support vector machines (SVM) and the hidden Markov model (HMM) struggle with computational overhead and scalability in large datasets, limiting their performance in critical healthcare applications. Additionally, existing methods often fail to provide accurate anomaly detection with low latency, making them unsuitable for time-sensitive environments. We infer the extraction, feature selection, attack detection, and data collection and processing procedures to anticipate anomaly inpatient data. We transfer the data, take care of missing values, and sanitize it using the pre-processing mechanism. We employed the recursive feature elimination (RFE) and dynamic principal component analysis (DPCA) algorithms for feature selection and extraction. In addition, we applied the Auto-encoded genetic recurrent neural network (AGRNN) approach to identify abnormalities. Data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and root mean square error (RMSE) are some metrics used to evaluate the proposed task.
用于临床决策的大数据分析已被应用于各个临床领域，因为临床决策更基于证据且前景广阔。医疗保健数据如此庞大且易于获取，以至于大数据分析彻底改变了这个领域并开辟了许多新前景。基于智能传感器的大数据分析推荐系统在使用传感器医学图像进行建议和监测时存在重大的隐私和安全问题。在云端发送和存储敏感医疗数据时，安全漏洞和未经授权访问的风险会增加，这可能导致身份盗窃和隐私侵犯。我们的努力将通过创建专门针对医学图像的基于机器学习的异常检测系统，并提供及时的治疗和通知，来改善患者护理和福祉。医疗保健系统中当前的异常检测方法，如人工智能和大数据分析 - 脑出血（AIBDA - ICH）以及并行共形神经网络（PCNN），面临着几个挑战，包括高资源消耗、低效的特征选择以及无法有效处理时间数据以进行实时监测。支持向量机（SVM）和隐马尔可夫模型（HMM）等技术在大型数据集中存在计算开销和可扩展性问题，限制了它们在关键医疗保健应用中的性能。此外，现有方法往往无法以低延迟提供准确的异常检测，使其不适合对时间敏感的环境。我们推断提取、特征选择、攻击检测以及数据收集和处理程序，以预测住院患者数据中的异常情况。我们传输数据，处理缺失值，并使用预处理机制对其进行清理。我们采用递归特征消除（RFE）和动态主成分分析（DPCA）算法进行特征选择和提取。此外，我们应用自动编码遗传递归神经网络（AGRNN）方法来识别异常情况。数据到达率、资源消耗、传播延迟、事务时期、真阳性率、误报率和均方根误差（RMSE）是用于评估所提出任务的一些指标。
