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Applied Sciences
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Smart Healthcare: Techniques, Applications and Prospects
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Smart Healthcare: Techniques, Applications and Prospects

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

Deadline for manuscript submissions: 10 July 2025 | Viewed by 845

Special Issue Editor

Dr. Emi Yuda

E-Mail Website
Guest Editor
1. Innovation Center For Semiconductor And Digital Future, Mie University, Tsu 514-8507, Japan
2. Center for Data-driven Science and Artificial Intelligence Tohoku University, Sendai 980-8579, Japan
Interests: bio-signal processing; big data analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The challenges of modern society, including population aging, insufficient medical services, and a declining workforce due to low birthrates, have increased demand for innovative solutions in healthcare. In this context, smart healthcare will play a pivotal role in supplementing and improving health and medical services. Smart healthcare is revolutionizing daily healthcare practices and behaviors. Cutting-edge technologies such as AI and IoT have the potential to significantly alleviate the burden on personal health management and medical care systems.

This Special Issue, "Smart Healthcare: Technologies, Applications and Prospects", will bring together the latest achievements and insights in this field, exploring its future possibilities from an academic perspective. This Special Issue focuses on three major areas of smart healthcare and compiles papers addressing each domain:

  1. Technological Developments:

    Rapid advancements in machine learning, wearable technology, and biosensor monitoring.

  2. Practical Applications:

    Telemedicine in regions with labor shortages, diagnostic support systems, and health management applications.

  3. Future Prospects:

    Responses to societal issues and the harmonious coexistence of technological progress and human well-being.

Dr. Emi Yuda
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.

Keywords

  • smart healthcare
  • machine learning
  • wearable technology
  • biosensor monitoring
  • health management

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Published Papers (1 paper)

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Research

27 pages, 3764 KiB  
Article
Effective Epileptic Seizure Detection with Hybrid Feature Selection and SMOTE-Based Data Balancing Using SVM Classifier
by Hany F. Atlam, Gbenga Ebenezer Aderibigbe and Muhammad Shahroz Nadeem
Appl. Sci. 2025, 15(9), 4690; https://doi.org/10.3390/app15094690 - 23 Apr 2025
Viewed by 148
Abstract
Epileptic seizures, a leading cause of global morbidity and mortality, pose significant challenges in timely diagnosis and management. Epilepsy, a chronic neurological disorder characterized by recurrent and unpredictable seizures, affects over 70 million people worldwide, according to the World Health Organization (WHO). Despite [...] Read more.
Epileptic seizures, a leading cause of global morbidity and mortality, pose significant challenges in timely diagnosis and management. Epilepsy, a chronic neurological disorder characterized by recurrent and unpredictable seizures, affects over 70 million people worldwide, according to the World Health Organization (WHO). Despite significant advances in medical science, accurate and timely diagnosis of epileptic seizures remains a challenge, with misdiagnosis rates reported to be as high as 30%. The consequences of misdiagnosis or delayed diagnosis can be severe, leading to increased morbidity, mortality, and reduced quality of life for patients. Therefore, this paper presents a novel approach to enhancing epileptic seizure detection through the integration of Synthetic Minority Over-Sampling Technique (SMOTE) for data balancing and a Hybrid Feature Selection Technique—Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT). The proposed model aims to improve the accuracy and reliability of seizure detection systems by addressing data imbalance and extracting discriminative features from electroencephalograms (EEG) signals. Experimental results demonstrate substantial performance gains, with the Support Vector Machine (SVM) classifier achieving 97.30% accuracy, 99.62% Area Under the Curve (AUC), and 93.08% F1 score, which outperform the results of the existing studies from the literature. The results highlight the effectiveness of the proposed model in advancing seizure detection systems, highlighting the potential to improve diagnostic capabilities and patient outcomes. Full article
(This article belongs to the Special Issue Smart Healthcare: Techniques, Applications and Prospects)
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