Department of Agricultural Sciences, Food, Natural Resources, and Engineering University of Foggia, Foggia, Italy.
Department of Economics, University of Bari, Bari, Italy.
Comput Biol Med. 2024 Dec;183:109330. doi: 10.1016/j.compbiomed.2024.109330. Epub 2024 Oct 26.
Conditions affecting the circulatory system and blood vessels are referred to as cardiovascular diseases that include strokes and heart attacks. Internet of Things (IoT) technologies monitor health metrics, identify irregularities and enable remote patient care, resulting in earlier intervention and more individualized therapy. This research aims to establish an efficient cardiovascular disease prediction model through Artificial intelligence (AI)-driven IoT technology. We propose a novel Shuffled Frog leaping-tuned Iterative Improved Adaptive Boosting (SF-IIAdaboost) algorithm for predicting cardiovascular disease with the implementation of IoT device data. IoT medical sensors and wearable devices will collect the patient's clinical data in our proposed framework. Z-score normalization is used to preprocess the gathered data and optimize its quality. Kernel principal component analysis (Kernel-PCA) extracts the relevant features from the processed data. We obtained a dataset that contains various health data gathered from numerous sensing devices to train our recommended model. Our proposed methodology is implemented using Python software. During the evaluation phase, we assess the effectiveness of our model across different parameters. We conduct comparative analyses against conventional methods to ascertain the superiority of our approach. Experimental findings demonstrate the superior performance of our recognition method over traditional approaches. The proposed SF-IIAdaboost algorithm, integrated with IoT device data, presents a promising avenue for predicting cardiovascular disease. The SF-IIAdaboost model demonstrated notable enhancements, attaining 95.37 % accuracy, 93.51 % precision, 94.3 % sensitivity, 96.31 % specificity, and 95.72 % F-measure. Future developments are predicted to involve computing on the edge, where immediate evaluations can be performed in the edge layer to avoid the basic constraints of the clouds, such as high latency, utilization of bandwidth and performing the growth of IoT data. Edge computing can revolutionize the healthcare industry's efficacy by enabling providers to make flexible decisions, operate quickly, and accurately anticipate diseases. It can improve the average level of service standards.
影响循环系统和血管的病症被称为心血管疾病，其中包括中风和心脏病发作。物联网 (IoT) 技术可监测健康指标、识别异常情况并实现远程患者护理，从而实现更早的干预和更个性化的治疗。本研究旨在通过人工智能 (AI) 驱动的物联网技术建立一种有效的心血管疾病预测模型。我们提出了一种新颖的 Shuffled Frog leaping-tuned Iterative Improved Adaptive Boosting (SF-IIAdaboost) 算法，用于通过物联网设备数据预测心血管疾病。在我们提出的框架中，物联网医疗传感器和可穿戴设备将收集患者的临床数据。Z 分数标准化用于预处理收集的数据并优化其质量。核主成分分析 (Kernel-PCA) 从处理后的数据中提取相关特征。我们获得了一个包含从众多传感设备收集的各种健康数据的数据集，以训练我们推荐的模型。我们的建议方法是使用 Python 软件实现的。在评估阶段，我们根据不同的参数评估模型的有效性。我们对传统方法进行了对比分析，以确定我们方法的优越性。实验结果表明，我们的识别方法优于传统方法。基于物联网设备数据的提出的 SF-IIAdaboost 算法为预测心血管疾病提供了一种很有前途的方法。SF-IIAdaboost 模型表现出了显著的改进，达到了 95.37%的准确率、93.51%的精度、94.3%的灵敏度、96.31%的特异性和 95.72%的 F1 分数。预计未来的发展将涉及边缘计算，其中可以在边缘层进行即时评估，以避免云的基本限制，例如高延迟、带宽利用和执行物联网数据的增长。边缘计算可以通过使提供者能够灵活地做出决策、快速运作和准确地预测疾病，从而彻底改变医疗保健行业的效果。它可以提高平均服务标准水平。
