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Machine Learning for Digital Health and Bioinformatics
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Machine Learning for Digital Health and Bioinformatics

A special issue of Applied System Innovation (ISSN 2571-5577). This special issue belongs to the section "Medical Informatics and Healthcare Engineering".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 12928

Special Issue Editor

Dr. Matloob Khushi

E-Mail Website
Guest Editor
1. School of EAST, University of Suffolk, Ipswich, UK
2. School of Computer Science, The University of Sydney, Sydney, Australia
Interests: data science; machine learning; deep learning; health informatics; digital health; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in Artificial Intelligence methods, such as machine/deep learning and natural language processing, have garnered unprecedented attention in recent years, resulting in substantial shifts in traditional health practices. The growing volume of big medical data is opening up new avenues for the advancement of digital health systems.

These big datasets, including but limited to clinical notes, patient treatment, and outcome history, can guide future practices using smart algorithms. This issue includes theoretical, analytical, and empirical research, as well as extensive reviews of pertinent research, a conceptual framework, and case studies of practical implementations in machine/deep learning for digital health and bioinformatics.

The following potential topics may be considered for publication; however, any related topic could be considered:

  • Analysis of big data for digital health;
  • Bioinformatics methods;
  • Artificial intelligence methods for health informatics;
  • Natural language processing for public health surveillance;
  • Deep learning and machine learning for medical data.

Dr. Matloob Khushi
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 System Innovation 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 1400 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

  • digital health
  • bioinformatics health informatics
  • NLP for public health
  • machine learning for digital health
  • deep learning for health informatics

Published Papers (4 papers)

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Research

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21 pages, 3291 KiB  
Article
Smart Diet Diary: Real-Time Mobile Application for Food Recognition
by Muhammad Nadeem, Henry Shen, Lincoln Choy and Julien Moussa H. Barakat
Appl. Syst. Innov. 2023, 6(2), 53; https://doi.org/10.3390/asi6020053 - 20 Apr 2023
Cited by 12 | Viewed by 4851
Abstract
Growing obesity has been a worldwide issue for several decades. This is the outcome of common nutritional disorders which results in obese individuals who are prone to many diseases. Managing diet while simultaneously dealing with the obligations of a working adult can be [...] Read more.
Growing obesity has been a worldwide issue for several decades. This is the outcome of common nutritional disorders which results in obese individuals who are prone to many diseases. Managing diet while simultaneously dealing with the obligations of a working adult can be difficult. This paper presents the design and development of a smartphone-based diet-tracking application, Smart Diet Diary, to assist obese people as well as patients to manage their dietary intake for a healthier life. The proposed system uses deep learning to recognize a food item and calculate its nutritional value in terms of calorie count. The dataset used comprises 16,000 images of food items belonging to 14 different categories to train a multi-label classifier. We applied a pre-trained faster R-CNN model for classification and achieved an overall accuracy of approximately 80.1% and an average calorie computation within 10% of the real calorie value. Full article
(This article belongs to the Special Issue Machine Learning for Digital Health and Bioinformatics)
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10 pages, 2282 KiB  
Article
Bone Anomaly Detection by Extracting Regions of Interest and Convolutional Neural Networks
by Maytham N. Meqdad, Hafiz Tayyab Rauf and Seifedine Kadry
Appl. Syst. Innov. 2023, 6(1), 21; https://doi.org/10.3390/asi6010021 - 2 Feb 2023
Cited by 1 | Viewed by 3621
Abstract
The most suitable method for assessing bone age is to check the degree of maturation of the ossification centers in the radiograph images of the left wrist. So, a lot of effort has been made to help radiologists and provide reliable automated methods [...] Read more.
The most suitable method for assessing bone age is to check the degree of maturation of the ossification centers in the radiograph images of the left wrist. So, a lot of effort has been made to help radiologists and provide reliable automated methods using these images. This study designs and tests Alexnet and GoogLeNet methods and a new architecture to assess bone age. All these methods are implemented fully automatically on the DHA dataset including 1400 wrist images of healthy children aged 0 to 18 years from Asian, Hispanic, Black, and Caucasian races. For this purpose, the images are first segmented, and 4 different regions of the images are then separated. Bone age in each region is assessed by a separate network whose architecture is new and obtained by trial and error. The final assessment of bone age is performed by an ensemble based on the Average algorithm between 4 CNN models. In the section on results and model evaluation, various tests are performed, including pre-trained network tests. The better performance of the designed system compared to other methods is confirmed by the results of all tests. The proposed method achieves an accuracy of 83.4% and an average error rate of 0.1%. Full article
(This article belongs to the Special Issue Machine Learning for Digital Health and Bioinformatics)
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13 pages, 2348 KiB  
Article
Hybrid Framework for Diabetic Retinopathy Stage Measurement Using Convolutional Neural Network and a Fuzzy Rules Inference System
by Rawan Ghnemat
Appl. Syst. Innov. 2022, 5(5), 102; https://doi.org/10.3390/asi5050102 - 14 Oct 2022
Cited by 8 | Viewed by 1945
Abstract
Diabetic retinopathy (DR) is an increasingly common eye disorder that gradually damages the retina. Identification at the early stage can significantly reduce the severity of vision loss. Deep learning techniques provide detection for retinal images based on data size and quality, as the [...] Read more.
Diabetic retinopathy (DR) is an increasingly common eye disorder that gradually damages the retina. Identification at the early stage can significantly reduce the severity of vision loss. Deep learning techniques provide detection for retinal images based on data size and quality, as the error rate increases with low-quality images and unbalanced data classes. This paper proposes a hybrid intelligent framework of a conventional neural network and a fuzzy inference system to measure the stages of DR automatically, Diabetic Retinopathy Stage Measurement using Conventional Neural Network and Fuzzy Inference System (DRSM-CNNFIS). The fuzzy inference used human experts’ rules to overcome data dependency problems. At first, the Conventional Neural Network (CNN) model was used for feature extraction, and then fuzzy rules were used to measure diabetic retinopathy stage percentage. The framework is trained using images from Kaggle datasets (Diabetic Retinopathy Detection, 2022). The efficacy of this framework outperformed the other models with regard to accuracy, macro average precision, macro average recall, and macro average F1 score: 0.9281, 0.7142, 0.7753, and 0.7301, respectively. The evaluation results indicate that the proposed framework, without any segmentation process, has a similar performance for all the classes, while the other classification models (Dense-Net-201, Inception-ResNet ResNet-50, Xception, and Ensemble methods) have different levels of performance for each class classification. Full article
(This article belongs to the Special Issue Machine Learning for Digital Health and Bioinformatics)
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Review

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10 pages, 469 KiB  
Review
A Brief Review on Gender Identification with Electrocardiography Data
by Eduarda Sofia Bastos, Rui Pedro Duarte, Francisco Alexandre Marinho, Roman Rudenko, Hanna Vitaliyivna Denysyuk, Norberto Jorge Gonçalves, Eftim Zdravevski, Carlos Albuquerque, Nuno M. Garcia and Ivan Miguel Pires
Appl. Syst. Innov. 2022, 5(4), 81; https://doi.org/10.3390/asi5040081 - 16 Aug 2022
Viewed by 1804
Abstract
Cardiac diseases have increased over the years; thus, it is essential to predict their possible signs. Accurate prediction efficiently treats the patient’s medical history before the attack occurs. Sensors available in commonly used devices may strive for the proper and early identification of [...] Read more.
Cardiac diseases have increased over the years; thus, it is essential to predict their possible signs. Accurate prediction efficiently treats the patient’s medical history before the attack occurs. Sensors available in commonly used devices may strive for the proper and early identification of various cardiac diseases. The primary purpose of this review is to analyze studies related to gender discretization based on data from different sensors including electrocardiography and echocardiography. The analyzed studies were published between 2010 and 2022 in various scientific databases, including PubMed Central, Springer, ACM, IEEE Xplore, MDPI, and Elsevier, based on the analysis of different cardiovascular diseases. It was possible to verify that most of the analyzed studies measured similar parameters as traditional methods including the QRS complex and other waves that characterize the various individuals. Full article
(This article belongs to the Special Issue Machine Learning for Digital Health and Bioinformatics)
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