E-DigitTool: A New-Fangled Framework for Disease Prediction and Diagnosis in Remote Healthcare Applications

IF 1.5 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
R. Lakshmi Priya, Varkuti Kumaraswamy, N. Kins Burk Sunil, S. Ramani, Sahukar Latha
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引用次数: 0

Abstract

The seamless communication between people and objects made possible by the Internet of Things (IoT) greatly improves our quality of life. It is especially important in the remote healthcare industry, where cutting-edge machine learning and artificial intelligence approaches are having a big impact. These analytics have the power to turn a proactive healthcare campaign from one that is reactive. For remote healthcare applications, this research study suggests an innovative framework called E-DigitTool to precisely identify and diagnose cardiovascular disorders. The digital health records collected by IoT sensors are preprocessed by the system using a Kalman filtering technique. The preprocessed medical data is analyzed using a modern optimization technique called Sine Cosine Optimized Feature Selection (SCO-FS) to identify the most significant features. Based on the chosen attributes, a state-of-the-art classification technology called Weighted Mean Vector Neural Network (WMVNN) is employed to accurately determine the type of sickness. Moreover, an Adaptive Wind Driven Optimization (AWDO) is used to compute the loss function optimum during illness classification, improving the performance and accuracy of the classifier. The main conclusions of the study show that E-DigitTool can analyze massive volumes of medical data with a performance accuracy of up to 99.5% for all datasets, resulting in an error rate of 0.5% and average precision, recall, and F1-score of 99%.

Abstract Image

电子数字工具:远程医疗应用中的疾病预测和诊断新框架
物联网(IoT)实现了人与物体之间的无缝通信,大大提高了我们的生活质量。这在远程医疗保健行业尤为重要,尖端的机器学习和人工智能方法正在对该行业产生重大影响。这些分析方法能够将主动式医疗保健活动从被动式转变为主动式。针对远程医疗保健应用,本研究提出了一个名为 E-DigitTool 的创新框架,用于精确识别和诊断心血管疾病。系统利用卡尔曼滤波技术对物联网传感器收集的数字健康记录进行预处理。预处理后的医疗数据使用一种名为正余弦优化特征选择(SCO-FS)的现代优化技术进行分析,以确定最重要的特征。根据所选属性,采用最先进的分类技术--加权均值向量神经网络(WMVNN)来准确判断疾病类型。此外,在疾病分类过程中,还使用了自适应风驱动优化(AWDO)来计算最优损失函数,从而提高了分类器的性能和准确性。研究的主要结论表明,E-DigitTool 可以分析海量医疗数据,所有数据集的准确率高达 99.5%,错误率为 0.5%,平均精确度、召回率和 F1 分数均为 99%。
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来源期刊
CiteScore
5.50
自引率
4.20%
发文量
93
审稿时长
>12 weeks
期刊介绍: Transactions of Electrical Engineering is to foster the growth of scientific research in all branches of electrical engineering and its related grounds and to provide a medium by means of which the fruits of these researches may be brought to the attentionof the world’s scientific communities. The journal has the focus on the frontier topics in the theoretical, mathematical, numerical, experimental and scientific developments in electrical engineering as well as applications of established techniques to new domains in various electical engineering disciplines such as: Bio electric, Bio mechanics, Bio instrument, Microwaves, Wave Propagation, Communication Theory, Channel Estimation, radar & sonar system, Signal Processing, image processing, Artificial Neural Networks, Data Mining and Machine Learning, Fuzzy Logic and Systems, Fuzzy Control, Optimal & Robust ControlNavigation & Estimation Theory, Power Electronics & Drives, Power Generation & Management The editors will welcome papers from all professors and researchers from universities, research centers, organizations, companies and industries from all over the world in the hope that this will advance the scientific standards of the journal and provide a channel of communication between Iranian Scholars and their colleague in other parts of the world.
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