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Diagnostics
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Machine Learning and Deep learning for Healthcare Data Processing and...
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Machine Learning and Deep learning for Healthcare Data Processing and Analyzing

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 3374

Special Issue Editors

Dr. Rajesh K. Tripathy

E-Mail Website
Guest Editor
BITS Pilani, Hyderabad, 500078, India
Interests: healthcare data; machine learning; deep learning; signal processing and image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Haipeng Liu

E-Mail Website
Guest Editor
Faculty of Health and Life Sciences, Coventry University, Coventry, CV1 5FB, UK
Interests: computational simulation; wearable sensing; cardiovascular diseases; medical data analysis

Special Issue Information

Dear Colleagues,

Healthcare data processing refers to the recording, storage, analysis and management of physiological data related to the healthcare industry. In the COVID-19 pandemic, AI-assisted diagnostics played an important role in the early detection of different pathologies and fine-grained classification of patients. The electronic medical records (EHRs) and AI algorithms are reshaping modern diagnostics, making precise medicine and data-driven healthcare in the big data era a reality. The healthcare data are recorded from the patients using biomedical signal recording instruments and medical imaging modalities, as well as wearable sensors. The automated analysis of healthcare data using AI algorithms is important for the diagnosis of various diseases. This Special Issue will help to demonstrate the applications of machine learning and deep learning for different healthcare data processing. This Special Issue welcomes high-quality original research papers and review papers on the applications of machine learning and deep learning methods for healthcare data analysis. We expect submissions of articles related but not limited to the following topics:

  1. Machine learning coupled with signal processing for electrocardiogram (ECG) data processing;
  2. Plethysmogram (PPG) data processing using machine learning coupled with signal processing;
  3. Electroencephalogram (EEG) data processing using signal processing and machine learning;
  4. Deep learning for EEG, ECG and PPG data processing;
  5. Machine learning and deep learning for medical image processing;
  6. Multimodal physiological data analysis using machine and deep learning techniques;
  7. Data-driven healthcare systems, meta-learning and multi-task learning for healthcare data analysis;
  8. Federated learning in healthcare data processing;
  9. Internet of Medical Things and Biomedical Embedded systems.

Dr. Rajesh K. Tripathy
Dr. Haipeng Liu
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. Diagnostics 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 2600 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

  • machine learning
  • deep learning
  • artificial intelligence (AI)
  • AI-assisted diagnostics
  • multimodal clinical data
  • data-driven healthcare

Published Papers (3 papers)

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Research

16 pages, 6690 KiB  
Article
Critical Risk Assessment, Diagnosis, and Survival Analysis of Breast Cancer
by Shamiha Binta Manir and Priya Deshpande
Diagnostics 2024, 14(10), 984; https://doi.org/10.3390/diagnostics14100984 - 8 May 2024
Viewed by 321
Abstract
Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using [...] Read more.
Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset before using various machine learning techniques can affect the performance of the classifiers. The three breast cancer datasets were examined using a variety of pre-processing approaches and classification models to assess their performance in terms of accuracy, precision, F-1 scores, etc. The PCA (principal component analysis) and resampling strategies produced remarkable results. For the BCSC Dataset, the Random Forest algorithm exhibited the best performance out of the applied classifiers, with an accuracy of 87.53%. Out of the different resampling techniques applied to the training dataset for training the Random Forest classifier, the Tomek Link exhibited the best test accuracy, at 87.47%. We compared all the models used with previously used techniques. After applying the resampling techniques, the accuracy scores of the test data decreased even if the training data accuracy increased. For the Breast Cancer Wisconsin diagnostic dataset, the K-Nearest Neighbor algorithm had the best accuracy with the original dataset test set, at 94.71%, and the PCA dataset test set exhibited 95.29% accuracy for detecting breast cancer. Using the SEER Dataset, this study also explores survival analysis, employing supervised and unsupervised learning approaches to offer insights into the variables affecting breast cancer survivability. This study emphasizes the significance of individualized approaches in the management and treatment of breast cancer by incorporating phenotypic variations and recognizing the heterogeneity of the disease. Through data-driven insights and advanced machine learning, this study contributes significantly to the ongoing efforts in breast cancer research, diagnostics, and personalized medicine. Full article
(This article belongs to the Special Issue Machine Learning and Deep learning for Healthcare Data Processing and Analyzing)
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26 pages, 2419 KiB  
Article
Conceptually Funded Usability Evaluation of an Application for Leveraging Descriptive Data Analysis Models for Cardiovascular Research
by Oliver Lohaj, Ján Paralič, Zuzana Pella, Dominik Pella and Adam Pavlíček
Diagnostics 2024, 14(9), 917; https://doi.org/10.3390/diagnostics14090917 - 28 Apr 2024
Viewed by 652
Abstract
The focus of this study, and the subject of this article, resides in the conceptually funded usability evaluation of an application of descriptive models to a specific dataset obtained from the East Slovak Institute of Heart and Vascular Diseases targeting cardiovascular patients. Delving [...] Read more.
The focus of this study, and the subject of this article, resides in the conceptually funded usability evaluation of an application of descriptive models to a specific dataset obtained from the East Slovak Institute of Heart and Vascular Diseases targeting cardiovascular patients. Delving into the current state-of-the-art practices, we examine the extent of cardiovascular diseases, descriptive data analysis models, and their practical applications. Most importantly, our inquiry focuses on exploration of usability, encompassing its application and evaluation methodologies, including Van Welie’s layered model of usability and its inherent advantages and limitations. The primary objective of our research was to conceptualize, develop, and validate the usability of an application tailored to supporting cardiologists’ research through descriptive modeling. Using the R programming language, we engineered a Shiny dashboard application named DESSFOCA (Decision Support System For Cardiologists) that is structured around three core functionalities: discovering association rules, applying clustering methods, and identifying association rules within predefined clusters. To assess the usability of DESSFOCA, we employed the System Usability Scale (SUS) and conducted a comprehensive evaluation. Additionally, we proposed an extension to Van Welie’s layered model of usability, incorporating several crucial aspects deemed essential. Subsequently, we rigorously evaluated the proposed extension within the DESSFOCA application with respect to the extended usability model, drawing insightful conclusions from our findings. Full article
(This article belongs to the Special Issue Machine Learning and Deep learning for Healthcare Data Processing and Analyzing)
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17 pages, 2311 KiB  
Article
A Novel Respiratory Rate Estimation Algorithm from Photoplethysmogram Using Deep Learning Model
by Wee Jian Chin, Ban-Hoe Kwan, Wei Yin Lim, Yee Kai Tee, Shalini Darmaraju, Haipeng Liu and Choon-Hian Goh
Diagnostics 2024, 14(3), 284; https://doi.org/10.3390/diagnostics14030284 - 28 Jan 2024
Viewed by 1073
Abstract
Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In [...] Read more.
Respiratory rate (RR) is a critical vital sign that can provide valuable insights into various medical conditions, including pneumonia. Unfortunately, manual RR counting is often unreliable and discontinuous. Current RR estimation algorithms either lack the necessary accuracy or demand extensive window sizes. In response to these challenges, this study introduces a novel method for continuously estimating RR from photoplethysmogram (PPG) with a reduced window size and lower processing requirements. To evaluate and compare classical and deep learning algorithms, this study leverages the BIDMC and CapnoBase datasets, employing the Respiratory Rate Estimation (RRest) toolbox. The optimal classical techniques combination on the BIDMC datasets achieves a mean absolute error (MAE) of 1.9 breaths/min. Additionally, the developed neural network model utilises convolutional and long short-term memory layers to estimate RR effectively. The best-performing model, with a 50% train–test split and a window size of 7 s, achieves an MAE of 2 breaths/min. Furthermore, compared to other deep learning algorithms with window sizes of 16, 32, and 64 s, this study’s model demonstrates superior performance with a smaller window size. The study suggests that further research into more precise signal processing techniques may enhance RR estimation from PPG signals. Full article
(This article belongs to the Special Issue Machine Learning and Deep learning for Healthcare Data Processing and Analyzing)
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