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Algorithms
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Machine Learning in Healthcare and Biomedical Application II
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Machine Learning in Healthcare and Biomedical Application II

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (15 April 2023) | Viewed by 21429

Special Issue Editor

Dr. Alessia Sarica

E-Mail Website
Guest Editor
Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
Interests: interpretable machine learning; explainable artificial intelligence; computer aided diagnosis; neuroimaging; neuroscience; neurodegenerative diseases prediction; brain MRI; tractography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the first volume of the present Special Issue, scholars presented their original scientific works on the topic “Machine Learning in Healthcare and Biomedical Application” and they demonstrated how Machine Learning (ML) could represent a valuable support in clinical decisions. Given the success of this collection, we invite you to participate to the 2nd volume of the present Special Issue, which focuses on the revolutionary changes to medicine brought about by ML. In particular, we aim to present the most recent discoveries on the application of new or state-of-the-art ML algorithms (e.g., supervised and unsupervised learning; feature selection, extraction and reduction; ensemble learning; deep learning; interpretability and explainability of ML models) in areas related, but not limited to · Disease identification, differential diagnosis, and prognosis; · Bioimage processing and analysis; · Emotion recognition in healthcare; · Cognitive and psychological profiling; · Epidemic outbreak prediction; · Personalized medicine. Contributions such as systematic reviews or meta-analyses on the above-mentioned topics are also welcome.

Dr. Alessia Sarica
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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 for healthcare
  •  computer-aided diagnosis
  •  automatic clinical decision
  •  epidemic outbreak prediction algorithms
  •  personalized medicine

Published Papers (3 papers)

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Research

25 pages, 3991 KiB  
Article
Method for Determining the Dominant Type of Human Breathing Using Motion Capture and Machine Learning
by Yulia Orlova, Alexander Gorobtsov, Oleg Sychev, Vladimir Rozaliev, Alexander Zubkov and Anastasia Donsckaia
Algorithms 2023, 16(5), 249; https://doi.org/10.3390/a16050249 - 12 May 2023
Cited by 3 | Viewed by 1995
Abstract
Since the COVID-19 pandemic, the demand for respiratory rehabilitation has significantly increased. This makes developing home (remote) rehabilitation methods using modern technology essential. New techniques and tools, including wireless sensors and motion capture systems, have been developed to implement remote respiratory rehabilitation. Significant [...] Read more.
Since the COVID-19 pandemic, the demand for respiratory rehabilitation has significantly increased. This makes developing home (remote) rehabilitation methods using modern technology essential. New techniques and tools, including wireless sensors and motion capture systems, have been developed to implement remote respiratory rehabilitation. Significant attention during respiratory rehabilitation is paid to the type of human breathing. Remote rehabilitation requires the development of automated methods of breath analysis. Most currently developed methods for analyzing breathing do not work with different types of breathing. These methods are either designed for one type (for example, diaphragmatic) or simply analyze the lungs’ condition. Developing methods of determining the types of human breathing is necessary for conducting remote respiratory rehabilitation efficiently. We propose a method of determining the type of breathing using wireless sensors with the motion capture system. To develop that method, spectral analysis and machine learning methods were used to detect the prevailing spectrum, the marker coordinates, and the prevailing frequency for different types of breathing. An algorithm for determining the type of human breathing is described. It is based on approximating the shape of graphs of distances between markers using sinusoidal waves. Based on the features of the resulting waves, we trained machine learning models to determine the types of breathing. After the first stage of training, we found that the maximum accuracy of machine learning models was below 0.63, which was too low to be reliably used in respiratory rehabilitation. Based on the analysis of the obtained accuracy, the training and running time of the models, and the error function, we choose the strategy of achieving higher accuracy by increasing the training and running time of the model and using a two-stage method, composed of two machine learning models, trained separately. The first model determines whether the breath is of the mixed type; if it does not predict the mixed type of breathing, the second model determines whether breathing is thoracic or abdominal. The highest accuracy achieved by the composite model was 0.81, which surpasses single models and is high enough for use in respiratory rehabilitation. Therefore, using three wireless sensors placed on the patient’s body and a two-stage algorithm using machine learning models, it was possible to determine the type of human breathing with high enough precision to conduct remote respiratory rehabilitation. The developed algorithm can be used in building rehabilitation applications. Full article
(This article belongs to the Special Issue Machine Learning in Healthcare and Biomedical Application II)
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19 pages, 1087 KiB  
Article
A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
by Md Ishtyaq Mahmud, Muntasir Mamun and Ahmed Abdelgawad
Algorithms 2023, 16(4), 176; https://doi.org/10.3390/a16040176 - 23 Mar 2023
Cited by 56 | Viewed by 16523
Abstract
Creating machines that behave and work in a way similar to humans is the objective of artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving, computer activities with artificial intelligence include other activities. A group of algorithms called “deep learning” is [...] Read more.
Creating machines that behave and work in a way similar to humans is the objective of artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving, computer activities with artificial intelligence include other activities. A group of algorithms called “deep learning” is used in machine learning. With the aid of magnetic resonance imaging (MRI), deep learning is utilized to create models for the detection and categorization of brain tumors. This allows for the quick and simple identification of brain tumors. Brain disorders are mostly the result of aberrant brain cell proliferation, which can harm the structure of the brain and ultimately result in malignant brain cancer. The early identification of brain tumors and the subsequent appropriate treatment may lower the death rate. In this study, we suggest a convolutional neural network (CNN) architecture for the efficient identification of brain tumors using MR images. This paper also discusses various models such as ResNet-50, VGG16, and Inception V3 and conducts a comparison between the proposed architecture and these models. To analyze the performance of the models, we considered different metrics such as the accuracy, recall, loss, and area under the curve (AUC). As a result of analyzing different models with our proposed model using these metrics, we concluded that the proposed model performed better than the others. Using a dataset of 3264 MR images, we found that the CNN model had an accuracy of 93.3%, an AUC of 98.43%, a recall of 91.19%, and a loss of 0.25. We may infer that the proposed model is reliable for the early detection of a variety of brain tumors after comparing it to the other models. Full article
(This article belongs to the Special Issue Machine Learning in Healthcare and Biomedical Application II)
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29 pages, 14391 KiB  
Article
Extending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes
by Liubov O. Elkhovskaya, Alexander D. Kshenin, Marina A. Balakhontceva, Mikhail V. Ionov and Sergey V. Kovalchuk
Algorithms 2023, 16(1), 57; https://doi.org/10.3390/a16010057 - 15 Jan 2023
Cited by 4 | Viewed by 1823
Abstract
Within process mining, discovery techniques make it possible to construct business process models automatically from event logs. However, results often do not achieve a balance between model complexity and fitting accuracy, establishing a need for manual model adjusting. This paper presents an approach [...] Read more.
Within process mining, discovery techniques make it possible to construct business process models automatically from event logs. However, results often do not achieve a balance between model complexity and fitting accuracy, establishing a need for manual model adjusting. This paper presents an approach to process mining that provides semi-automatic support to model optimization based on the combined assessment of model complexity and fitness. To balance complexity and fitness, a model simplification approach is proposed, which abstracts the raw model at the desired granularity. Additionally, we introduce a concept of meta-states, a cycle collapsing in the model, which can potentially simplify the model and interpret it. We aim to demonstrate the capabilities of our technological solution using three datasets from different applications in the healthcare domain. These are remote monitoring processes for patients with arterial hypertension and workflows of healthcare workers during the COVID-19 pandemic. A case study also investigates the use of various complexity measures and different ways of solution application, providing insights on better practices in improving interpretability and complexity/fitness balance in process models. Full article
(This article belongs to the Special Issue Machine Learning in Healthcare and Biomedical Application II)
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