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Interdisciplinary Applications: Data and AI Technologies for Healthcare and Biomedicine...
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Interdisciplinary Applications: Data and AI Technologies for Healthcare and Biomedicine for/in Human Life

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 9165

Special Issue Editors

Prof. Dr. Keun Ho Ryu

E-Mail Website
Guest Editor
Department of Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea
Interests: big data and databases; data mining; biomedical informatics; and bioinformatics; deep learning and interdisciplinary applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nipon Theera-Umpon

E-Mail Website
Guest Editor
1. Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
2. Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand
Interests: pattern recognition; digital image processing; neural networks; fuzzy sets and systems; big data analysis; data mining; medical signal and image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Kwang Sun Ryu

E-Mail Website
Guest Editor
Cancer Data Center, National Cancer Control Institute, National Cancer Center, Gyeonggi-do, Goyang-si 10408, Korea
Interests: medical informatics; data-mining

Special Issue Information

Dear Colleagues,

Interdisciplinary and their applications in technologies such as Artificial Intelligence (AI), databases and bio and medical research are now not only the hottest research topic and a new, challenging and practical area of research, but also close and very important to our life. These research studies are a most promising and an important technical field, that enriches our health and our future life.

Therefore, the goal of this Special thematic Issue is to explore how emerging technology solutions and real-world applications in human life, disease, cancer, healthcare, and hospitals can help human beings to lead heathy lives, as well as enhance wellbeing. Specifically, innovative contributions that either solve or advance the understanding of issues related to emerging technologies as well as interdisciplinary research and applications, as well as practical experiences in the real world, are very welcome. This Special thematic Issue also seeks to not only present solutions that combine state-of-the-art theoretical approaches like computational biology, data and computer software, and model-based approaches to exploit the large amount of health and bio, hospital, and human life data resources available, but also new methods which more generally describe the successful application of emerging technologies, sciences and engineering to issues such as disease, cancer, knowledgebase, databases, sensor device and user interfaces, software design, and system implementation in the medical domain, as well as in the healthcare, biology, and wellbeing domains.

 The sub-topics to be covered within the issue are provided here:

  • Machine and deep learning approaches to disease, cancer, and health data;
  • Decision support and recommend systems for healthcare and wellbeing;
  • Regression and forecasting for medical and/or biomedical signals;
  • Medical signal and image processing and techniques in healthcare and wellbeing systems;
  • Explainable AI and AI application technologies;
  • Platforms in medicine and healthcare, and biomedical and text-mining applications;
  • Interdisciplinary technologies: data, mining, AI, bio and medical, knowledge, and healthcare;
  • Emerging technology and theory, systems and techniques in human life, disease, cancer, healthcare, and hospital, etc.

Prof. Dr. Keun Ho Ryu
Prof. Dr. Nipon Theera-Umpon
Dr. Kwang Sun Ryu
Guest Editors

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

  • emerging and interdisciplinary technology
  • artificial intelligence
  • database and big data
  • disease
  • healthcare
  • biomedicine/biomedical
  • human life

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Published Papers (2 papers)

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Research

17 pages, 2030 KiB  
Article
Hypertension Prediction in Adolescents Using Anthropometric Measurements: Do Machine Learning Models Perform Equally Well?
by Soo See Chai, Kok Luong Goh, Whye Lian Cheah, Yee Hui Robin Chang and Giap Weng Ng
Appl. Sci. 2022, 12(3), 1600; https://doi.org/10.3390/app12031600 - 2 Feb 2022
Cited by 8 | Viewed by 2576
Abstract
The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle [...] Read more.
The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combination with other biophysical and lifestyle variables. It is essential to assess the impacts of the chosen machine learning models using simple anthropometric measurements. We developed and tested 13 machine learning methods of neural network, ensemble, and classical categories to predict hypertension in adolescents using only simple anthropometric measurements. The imbalanced dataset of 2461 samples with 30.1% hypertension subjects was first partitioned into 90% for training and 10% for validation. The training dataset was reduced to eight simple anthropometric measurements: age, C index, ethnicity, gender, height, location, parental hypertension, and waist circumference using correlation coefficient. The Synthetic Minority Oversampling Technique (SMOTE) combined with random under-sampling was used to balance the dataset. The models with optimal hyperparameters were assessed using accuracy, precision, sensitivity, specificity, F1-score, misclassification rate, and AUC on the testing dataset. Across all seven performance measures, no model consistently outperformed the others. LightGBM was the best model for all six performance metrics, except sensitivity, whereas Decision Tree was the worst. We proposed using Bayes’ Theorem to assess the models’ applicability in the Sarawak adolescent population, resulting in the top four models being LightGBM, Random Forest, XGBoost, and CatBoost, and the bottom four models being Logistic Regression, LogitBoost, SVM, and Decision Tree. This study demonstrates that the choice of machine learning models has an effect on the prediction outcomes. Full article
(This article belongs to the Special Issue Interdisciplinary Applications: Data and AI Technologies for Healthcare and Biomedicine for/in Human Life)
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14 pages, 1464 KiB  
Article
Emotion Recognition from ECG Signals Using Wavelet Scattering and Machine Learning
by Axel Sepúlveda, Francisco Castillo, Carlos Palma and Maria Rodriguez-Fernandez
Appl. Sci. 2021, 11(11), 4945; https://doi.org/10.3390/app11114945 - 27 May 2021
Cited by 56 | Viewed by 5662
Abstract
Affect detection combined with a system that dynamically responds to a person’s emotional state allows an improved user experience with computers, systems, and environments and has a wide range of applications, including entertainment and health care. Previous studies on this topic have used [...] Read more.
Affect detection combined with a system that dynamically responds to a person’s emotional state allows an improved user experience with computers, systems, and environments and has a wide range of applications, including entertainment and health care. Previous studies on this topic have used a variety of machine learning algorithms and inputs such as audial, visual, or physiological signals. Recently, a lot of interest has been focused on the last, as speech or video recording is impractical for some applications. Therefore, there is a need to create Human–Computer Interface Systems capable of recognizing emotional states from noninvasive and nonintrusive physiological signals. Typically, the recognition task is carried out from electroencephalogram (EEG) signals, obtaining good accuracy. However, EEGs are difficult to register without interfering with daily activities, and recent studies have shown that it is possible to use electrocardiogram (ECG) signals for this purpose. This work improves the performance of emotion recognition from ECG signals using wavelet transform for signal analysis. Features of the ECG signal are extracted from the AMIGOS database using a wavelet scattering algorithm that allows obtaining features of the signal at different time scales, which are then used as inputs for different classifiers to evaluate their performance. The results show that the proposed algorithm for extracting features and classifying the signals obtains an accuracy of 88.8% in the valence dimension, 90.2% in arousal, and 95.3% in a two-dimensional classification, which is better than the performance reported in previous studies. This algorithm is expected to be useful for classifying emotions using wearable devices. Full article
(This article belongs to the Special Issue Interdisciplinary Applications: Data and AI Technologies for Healthcare and Biomedicine for/in Human Life)
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