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Mathematics
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Recent Research in Using Mathematical Machine Learning in Medicine
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Recent Research in Using Mathematical Machine Learning in Medicine

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematical Biology".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 6795

Special Issue Editor

Dr. Kang Lu

E-Mail Website
Guest Editor
Department of Mathematics, University of Denver, Denver, CO 80210, USA
Interests: mathematical modeling; differential equations; machine learning

Special Issue Information

Dear Colleagues

Mathematical machine learning and artificial intelligence have made remarkable progress in several fields, especially medicine. Artificial intelligence refers to computational programs that mimic and simulate human intelligence in problem-solving and learning, such as a radiologist's ability to diagnose tumor progression or learn new patterns of lung cancer. In biomedical research, mathematical machine learning is a subset of artificial intelligence. It uses computer algorithms to discover information from raw data to make medically accurate and correct decisions. Mathematical machine learning increases the efficiency and reliability of computational processes and reduces costs. Furthermore, it can generate models accurately and quickly through data analysis by providing tools that can process vast amounts of data far beyond human comprehension.

The purpose of this Special Issue is to help researchers gain a proper understanding of machine learning and its applications in healthcare by gathering a collection of articles reflecting the latest developments in different fields of data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods, and applications (diagnosis, treatment).

Dr. Kang Lu
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. Mathematics 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

  • mathematical machine learning
  • mathematical oncology
  • biomedical modeling
  • deep learning
  • mathematical modeling

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Published Papers (3 papers)

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Research

17 pages, 2272 KiB  
Article
Prediction of ECOG Performance Status of Lung Cancer Patients Using LIME-Based Machine Learning
by Hung Viet Nguyen and Haewon Byeon
Mathematics 2023, 11(10), 2354; https://doi.org/10.3390/math11102354 - 18 May 2023
Cited by 4 | Viewed by 2543
Abstract
The Eastern Cooperative Oncology Group (ECOG) performance status is a widely used method for evaluating the functional abilities of cancer patients and predicting their prognosis. It is essential for healthcare providers to frequently assess the ECOG performance status of lung cancer patients to [...] Read more.
The Eastern Cooperative Oncology Group (ECOG) performance status is a widely used method for evaluating the functional abilities of cancer patients and predicting their prognosis. It is essential for healthcare providers to frequently assess the ECOG performance status of lung cancer patients to ensure that it accurately reflects their current functional abilities and to modify their treatment plan accordingly. This study aimed to develop and evaluate an AdaBoost classification (ADB-C) model to predict a lung cancer patient’s performance status following treatment. According to the results, the ADB-C model has the highest “Area under the receiver operating characteristic curve” (ROC AUC) score at 0.7890 which outperformed other benchmark models including Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forest, XGBoost, and TabNet. In order to achieve model prediction explainability, we combined the ADB-C model with a LIME-based explainable model. This explainable ADB-C model may assist medical professionals in exploring effective cancer treatments that would not negatively impact the post-treatment performance status of a patient. Full article
(This article belongs to the Special Issue Recent Research in Using Mathematical Machine Learning in Medicine)
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27 pages, 687 KiB  
Article
Machine Learning at the Service of Survival Analysis: Predictions Using Time-to-Event Decomposition and Classification Applied to a Decrease of Blood Antibodies against COVID-19
by Lubomír Štěpánek, Filip Habarta, Ivana Malá, Ladislav Štěpánek, Marie Nakládalová, Alena Boriková and Luboš Marek
Mathematics 2023, 11(4), 819; https://doi.org/10.3390/math11040819 - 6 Feb 2023
Cited by 3 | Viewed by 2257
Abstract
The Cox proportional hazard model may predict whether an individual belonging to a given group would likely register an event of interest at a given time. However, the Cox model is limited by relatively strict statistical assumptions. In this study, we propose decomposing [...] Read more.
The Cox proportional hazard model may predict whether an individual belonging to a given group would likely register an event of interest at a given time. However, the Cox model is limited by relatively strict statistical assumptions. In this study, we propose decomposing the time-to-event variable into “time” and “event” components and using the latter as a target variable for various machine-learning classification algorithms, which are almost assumption-free, unlike the Cox model. While the time component is continuous and is used as one of the covariates, i.e., input variables for various classification algorithms such as logistic regression, naïve Bayes classifiers, decision trees, random forests, and artificial neural networks, the event component is binary and thus may be modeled using these classification algorithms. Moreover, we apply the proposed method to predict a decrease or non-decrease of IgG and IgM blood antibodies against COVID-19 (SARS-CoV-2), respectively, below a laboratory cut-off, for a given individual at a given time point. Using train-test splitting of the COVID-19 dataset (n=663 individuals), models for the mentioned algorithms, including the Cox proportional hazard model, are learned and built on the train subsets while tested on the test ones. To increase robustness of the model performance evaluation, models’ predictive accuracies are estimated using 10-fold cross-validation on the split dataset. Even though the time-to-event variable decomposition might ignore the effect of individual data censoring, many algorithms show similar or even higher predictive accuracy compared to the traditional Cox proportional hazard model. In COVID-19 IgG decrease prediction, multivariate logistic regression (of accuracy 0.811), support vector machines (of accuracy 0.845), random forests (of accuracy 0.836), artificial neural networks (of accuracy 0.806) outperform the Cox proportional hazard model (of accuracy 0.796), while in COVID-19 IgM antibody decrease prediction, neither Cox regression nor other algorithms perform well (best accuracy is 0.627 for Cox regression). An accurate prediction of mainly COVID-19 IgG antibody decrease can help the healthcare system manage, with no need for extensive blood testing, to identify individuals, for instance, who could postpone boosting vaccination if new COVID-19 variant incomes or should be flagged as high risk due to low COVID-19 antibodies. Full article
(This article belongs to the Special Issue Recent Research in Using Mathematical Machine Learning in Medicine)
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13 pages, 1670 KiB  
Article
Identification of Systemic Sclerosis through Machine Learning Algorithms and Gene Expression
by Gerardo Alfonso Perez and Raquel Castillo
Mathematics 2022, 10(24), 4632; https://doi.org/10.3390/math10244632 - 7 Dec 2022
Cited by 3 | Viewed by 1324
Abstract
Systemic sclerosis (SSc) is an autoimmune, chronic disease that remains not well understood. It is believed that the cause of the illness is a combination of genetic and environmental factors. The evolution of the illness also greatly varies from patient to patient. A [...] Read more.
Systemic sclerosis (SSc) is an autoimmune, chronic disease that remains not well understood. It is believed that the cause of the illness is a combination of genetic and environmental factors. The evolution of the illness also greatly varies from patient to patient. A common complication of the illness, with an associated higher mortality, is interstitial lung disease (ILD). We present in this paper an algorithm (using machine learning techniques) that it is able to identify, with a 92.2% accuracy, patients suffering from ILD-SSc using gene expression data obtained from peripheral blood. The data were obtained from public sources (GEO accession GSE181228) and contains genetic data for 134 patients at an initial stage as well as at a follow up date (12 months later) for 98 of these patients. Additionally, there are 45 control (healthy) cases. The algorithm also identified 172 genes that might be involved in the illness. These 172 genes appeared in all the 20 most accurate classification models among a total of half a million models estimated. Their frequency might suggest that they are related to the illness to some degree. The proposed algorithm, besides differentiating between control and patients, was also able to distinguish among different variants of the illness (diffuse variants). This can have a significance from a treatment point of view. The different type of variants have a different associated prognosis. Full article
(This article belongs to the Special Issue Recent Research in Using Mathematical Machine Learning in Medicine)
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