Int. J. E Health Medical Commun.最新文献_第6页

Regulations and Standards Aware Framework for Recording of mHealth App Vulnerabilities 移动健康应用程序漏洞记录的法规和标准意识框架

Int. J. E Health Medical Commun. Pub Date : 2021-05-01 DOI: 10.4018/IJEHMC.20210501.OA1

Z. Prodanoff, Cynthia White-Williams, H. Chi

引用次数: 1

SLAMMP Framework for Cloud Resource Management and Its Impact on Healthcare Computational Techniques 云资源管理的SLAMMP框架及其对医疗保健计算技术的影响

Int. J. E Health Medical Commun. Pub Date : 2021-03-01 DOI: 10.4018/ijehmc.2021030101

V. Prasad, Madhuri D. Bhavsar

引用次数: 6

Artificial Bee Colony and Deep Neural Network-Based Diagnostic Model for Improving the Prediction Accuracy of Diabetes 基于人工蜂群和深度神经网络的糖尿病诊断模型提高预测精度

Int. J. E Health Medical Commun. Pub Date : 2021-03-01 DOI: 10.4018/ijehmc.2021030102

A. Srivastava, Y. Kumar, P. Singh

{"title":"Artificial Bee Colony and Deep Neural Network-Based Diagnostic Model for Improving the Prediction Accuracy of Diabetes","authors":"A. Srivastava, Y. Kumar, P. Singh","doi":"10.4018/ijehmc.2021030102","DOIUrl":"https://doi.org/10.4018/ijehmc.2021030102","url":null,"abstract":"A large number of machine learning approaches are implemented in healthcare field for effective diagnosis and prediction of different diseases. The aim of these machine learning approaches is to build automated diagnostic tool for helping the physician as well as monitor the health status of patients. These diagnostic tools are widely adopted in intensive care unit for life expectancy of patients. In this study, an effort is made to design an automated diagnostic model for the diagnosis and prediction of diabetes patients. The proposed diagnostic model is designed using artificial bee colony (ABC) algorithm and deep neural network (DNN) technique, called ABC-DNN-based diagnostic model. The ABC algorithm is applied to determine the relevant features for diabetes prediction and diagnosis while DNN technique is adopted for the prediction and diagnosis of diabetes affected patients. The performance of proposed diagnostic model is tested over Pima Indian Diabetes dataset and evaluated using accuracy, sensitivity, specificity, F-measure, Kappa, and area under curve (AUC) parameters. Further, 10-fold and 50-50% training-testing method are considered to assess the performance of proposed diagnostic model. The experimental results of proposed ABC-DNN model is compared with DNN technique and several existing diabetes studies. It is observed that proposed ABC-DNN model achieves 94.74% accuracy rate using 10-fold method.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129013288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Brain State Intelligence and Cognitive Health Through EEG Date Modeling 基于脑电图数据建模的脑状态智力与认知健康

Int. J. E Health Medical Commun. Pub Date : 2021-01-01 DOI: 10.4018/ijehmc.2021010104

Hong Lin, J. Garza, G. Schreiber, Minghao Yang, Yunwei Cui

引用次数: 0

Rummage of Machine Learning Algorithms in Cancer Diagnosis 癌症诊断中的机器学习算法综述

Int. J. E Health Medical Commun. Pub Date : 2021-01-01 DOI: 10.4018/ijehmc.2021010101

P. Johri, V. Saxena, Avneesh Kumar

{"title":"Rummage of Machine Learning Algorithms in Cancer Diagnosis","authors":"P. Johri, V. Saxena, Avneesh Kumar","doi":"10.4018/ijehmc.2021010101","DOIUrl":"https://doi.org/10.4018/ijehmc.2021010101","url":null,"abstract":"With the continuous improvement of digital imaging technology and rapid increase in the use of digital medical records in last decade, artificial intelligence has provided various techniques to analyze these data. Machine learning, a subset of artificial intelligence techniques, provides the ability to learn from past and present and to predict the future on the basis of data. Various AI-enabled support systems are designed by using machine learning algorithms in order to optimize and computerize the process of clinical decision making and to bring about a massive archetype change in the healthcare sector such as timely identification, revealing and treatment of disease, as well as outcome prediction. Machine learning algorithms are implemented in the healthcare sector and helped in diagnosis of critical illness such as cancer, neurology, cardiac, and kidney disease as well as with easing in anticipation of disease progression. By applying and executing machine learning algorithms over healthcare data, one can evaluate, analyze, and generate the results that can be used not only to advance the prior health studies but also to aid in forecasting a patient's chances of developing of various diseases. The aim in this article is to present an overview of machine learning and to cover various algorithms of machine learning and their present implementation in the healthcare sector.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127404034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Cascaded Dilated Deep Residual Network for Volumetric Liver Segmentation From CT Image 级联扩张深度残差网络在CT肝脏体积分割中的应用

Int. J. E Health Medical Commun. Pub Date : 2021-01-01 DOI: 10.4018/ijehmc.2021010103

G. K. Mourya, M. Gogoi, S. Talbar, Prasad Dutande, Ujjwal Baid

引用次数: 6

Human Fall Detection Using Efficient Kernel and Eccentric Approach 基于高效核和偏心方法的人体跌倒检测

Int. J. E Health Medical Commun. Pub Date : 2021-01-01 DOI: 10.4018/ijehmc.2021010105

Rashmi Shrivastava, Manju Pandey

引用次数: 2

fMRI Feature Extraction Model for ADHD Classification Using Convolutional Neural Network 基于卷积神经网络的ADHD分类fMRI特征提取模型

Int. J. E Health Medical Commun. Pub Date : 2021-01-01 DOI: 10.4018/ijehmc.2021010106

S. D. Silva, Sanuwani Dayarathna, G. Ariyarathne, D. Meedeniya, S. Jayarathna

引用次数: 13

Data Mining of MR Technical Parameters: A Case Study for SAR in a Large-Scale MR Repository 核磁共振技术参数的数据挖掘:以大型核磁共振库SAR为例

Int. J. E Health Medical Commun. Pub Date : 2021-01-01 DOI: 10.4018/ijehmc.2021010102

Adriana Murraças, Paula Martins, C. Ferreira, T. Godinho, Augusto Silva

引用次数: 0

Computer-Assisted Analysis of Human Semen Concentration and Motility 人类精液浓度和活力的计算机辅助分析

Int. J. E Health Medical Commun. Pub Date : 2020-10-01 DOI: 10.4018/IJEHMC.2020100102

K. Boumaza, A. Loukil

{"title":"Computer-Assisted Analysis of Human Semen Concentration and Motility","authors":"K. Boumaza, A. Loukil","doi":"10.4018/IJEHMC.2020100102","DOIUrl":"https://doi.org/10.4018/IJEHMC.2020100102","url":null,"abstract":"Computer-assisted semen analysis systems insist on evaluating sperm characteristics. These systems afford capacity to study and evaluate sperm statistical and morphological characteristics such as concentration, morphology, and motility, which have an important role in diagnosis and treatment of male infertility. In this paper, the proposed algorithm allows the assessment of concentration and motility rate of sperms in microscopic videos. First, enhancement process is required because of microscopic images limitations such as low contrast and noises. Then, for true sperm recognition among noise and debris, a hybrid approach is proposed using a combination between segmentation techniques. After, the use of geometric features of the bounding ellipse of the sperm head led to define sperm concentration. Finally, inter-frame difference is applied for motile sperm detection. The proposed method was tested on microscopic videos of human semen; the performance of this method is analyzed in terms of speed, accuracy, and complexity. Obtained results during the experiments are very promising compared with those obtained by the traditional assessment, which is the most widely used and approved in the laboratories.","PeriodicalId":375617,"journal":{"name":"Int. J. E Health Medical Commun.","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114340216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1