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Applied Sciences
Special Issues
Recent Developments in Smart Healthcare
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Recent Developments in Smart Healthcare

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (8 April 2020) | Viewed by 9098

Special Issue Editors

Prof. Dr. Wenbing Zhao

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA
Interests: fault-tolerant computing; computer and network security; peer-to-peer and grid computing; performance evaluation of distributed systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yonghong Peng

E-Mail Website
Guest Editor
Faculty of Computer Science, University of Sunderland, St Peter Campus, Sunderland SR6 0DD, UK
Interests: data science; machine learning; artificial intelligence; digital health and medical informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medicine is undergoing a sector-wide transformation thanks to advances in sensing, computing, networking, and algorithms. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to wellbeing-centered. In essence, healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics, to decision support for healthcare professionals through big data analytics, to facilitating behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, of higher quality, and a lower cost. In this Special Issue, we welcome original research, as well as review articles in all areas of smart healthcare.

Prof. Dr. Wenbing Zhao
Prof. Dr. Yonghong Peng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

22 pages, 1477 KiB  
Article
Evaluation of Quality and Readability of Online Health Information on High Blood Pressure Using DISCERN and Flesch-Kincaid Tools
by Muhammad Tahir, Muhammad Usman, Fazal Muhammad, Shams ur Rehman, Imran Khan, Muhammad Idrees, Muhammad Irfan and Adam Glowacz
Appl. Sci. 2020, 10(9), 3214; https://doi.org/10.3390/app10093214 - 05 May 2020
Cited by 11 | Viewed by 4631
Abstract
High Blood Pressure (BP) is a vital factor in the development of cardiovascular diseases worldwide. For more than a decade now, patients search for quality and easy-to-read Online Health Information (OHI) for symptoms, preventions, therapy and other medical conditions. In this paper, we [...] Read more.
High Blood Pressure (BP) is a vital factor in the development of cardiovascular diseases worldwide. For more than a decade now, patients search for quality and easy-to-read Online Health Information (OHI) for symptoms, preventions, therapy and other medical conditions. In this paper, we evaluate the quality and readability of OHI about high BP. In order that the first 20 clicks of three top-rated search engines have been used to collect the pertinent data. Using the exclusion criteria, 25 unique websites are selected for evaluation. The quality of all included links is evaluated through DISCERN checklist, a questionnaire for assessing the quality of written information for a health problem. To enhance the reliability of evaluation, all links are separately assessed by two different groups—a group of Health Professional (HPs) and a group of Lay Subjects (LS). A readability test is performed using Flesch-Kincaid tool. Fleiss’ kappa has been calculated before considering average value of each group. After evaluation, the average DISCERN value of HPs is 49.43 ± 14.0 (fair quality) while for LS, it is 48.7 ± 12.2; the mean Flesch-Reading Ease Score (FRES) is 58.5 ± 11.1, which is fairly difficult to read and the Average Grade Level (AGL) is 8.8 ± 1.9. None of the websites scored more than 73 (90%). In both groups, only 4 (16%) websites achieved DISCERN score over 80%. Mann-Whitney and Cronbach’s alpha have been computed to check the statistical significance of the difference between two groups and internal consistency of DISCERN checklist, respectively. Normality and homoscedasticity tests have been performed to check the distribution of scores of both evaluating groups. In both groups, information category websites achieved high DISCERN score but their readability level is worse. Highest scoring websites have clear aim, succinct source and high quality of information on treatment options. High BP is a pervasive disease, yet most of the websites did not produce precise or high-quality information on treatment options. Full article
(This article belongs to the Special Issue Recent Developments in Smart Healthcare)
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16 pages, 2109 KiB  
Article
Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare
by Tariq Sadad, Ayyaz Hussain, Asim Munir, Muhammad Habib, Sajid Ali Khan, Shariq Hussain, Shunkun Yang and Mohammed Alawairdhi
Appl. Sci. 2020, 10(6), 1900; https://doi.org/10.3390/app10061900 - 11 Mar 2020
Cited by 44 | Viewed by 3894
Abstract
Breast cancer is a highly prevalent disease in females that may lead to mortality in severe cases. The mortality can be subsided if breast cancer is diagnosed at an early stage. The focus of this study is to detect breast malignancy through computer-aided [...] Read more.
Breast cancer is a highly prevalent disease in females that may lead to mortality in severe cases. The mortality can be subsided if breast cancer is diagnosed at an early stage. The focus of this study is to detect breast malignancy through computer-aided diagnosis (CADx). In the first phase of this work, Hilbert transform is employed to reconstruct B-mode images from the raw data followed by the marker-controlled watershed transformation to segment the lesion. The methods based only on texture analysis are quite sensitive to speckle noise and other artifacts. Therefore, a hybrid feature set is developed after the extraction of shape-based and texture features from the breast lesion. Decision tree, k-nearest neighbor (KNN), and ensemble decision tree model via random under-sampling with Boost (RUSBoost) are utilized to segregate the cancerous lesions from the benign ones. The proposed technique is tested on OASBUD (Open Access Series of Breast Ultrasonic Data) and breast ultrasound (BUS) images collected at Baheya Hospital Egypt (BHE). The OASBUD dataset contains raw ultrasound data obtained from 100 patients containing 52 malignant and 48 benign lesions. The dataset collected at BHE contains 210 malignant and 437 benign images. The proposed system achieved promising accuracy of 97% with confidence interval (CI) of 91.48% to 99.38% for OASBUD and 96.6% accuracy with CI of 94.90% to 97.86% for the BHE dataset using ensemble method. Full article
(This article belongs to the Special Issue Recent Developments in Smart Healthcare)
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