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Mathematics
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Statistics and Probabilities and Their Role within Health Sciences
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Statistics and Probabilities and Their Role within Health Sciences

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: 10 April 2025 | Viewed by 1578

Special Issue Editor

Dr. Oluwagbohunmi A. Awosoga

E-Mail Website
Guest Editor
Faculty of Health Sciences, University of Lethbridge, Lethbridge, AB, Canada
Interests: repeated measures design; longitudinal studies; categorical data analysis; biostatistics; parametric and nonparametric methods; time series and survival data analysis

Special Issue Information

Dear Colleagues,

Statistics is the branch of mathematics that deals with data collection, analysis, interpretation, and presentation. It plays a crucial role in the health sciences by quantifying and assessing the effectiveness of medical treatments, identifying health trends, and evaluating the risk factors associated with different diseases. On the other hand, probability is the branch of mathematics that studies random events. It plays a critical role in the health sciences by enabling scientists to develop models to predict the likelihood of certain outcomes, such as the probability of a particular disease occurring in a given population. Statistics and probability form the basis of evidence-based medicine, using scientific evidence to guide medical decision making. They are used in designing clinical trials, analyzing research data, and evaluating the effectiveness of medical treatments.

This Special Issue will accept high-quality papers containing original research results and review articles of exceptional merit in the following fields:

  • Occupational safety and health;
  • Probability Theory; 
  • Categorical data analysis,
  • Biostatistics;
  • Parametric and nonparametric methods;
  • Time series and survival data analysis;
  • Probabilistic and statistical models;
  • Bayesian methods;
  • Environmental statistics;
  • Epidemiological methods.

Dr. Oluwagbohunmi A. Awosoga
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.

Published Papers (2 papers)

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Research

23 pages, 765 KiB  
Article
Algorithm for Determination of Indicators Predicting Health Status for Health Monitoring Process Optimization
by Aleksandras Krylovas, Natalja Kosareva and Stanislav Dadelo
Mathematics 2024, 12(8), 1232; https://doi.org/10.3390/math12081232 - 19 Apr 2024
Viewed by 257
Abstract
This article proposes an algorithm that allows the selection of prognostic variables from a set of 21 variables describing the health statuses of male and female students. The set of variables could be divided into two groups—body condition indicators and body activity indicators. [...] Read more.
This article proposes an algorithm that allows the selection of prognostic variables from a set of 21 variables describing the health statuses of male and female students. The set of variables could be divided into two groups—body condition indicators and body activity indicators. For this purpose, we propose applying the multiple criteria decision methods WEBIRA, entropy-ARAS, and SAW in modelling the general health index, a latent variable describing health status, which is used to rank the alternatives. In the next stage, applying multiple regression analysis, the most informative indicators influencing health status are selected by reducing the indicator’s number to 9–11, and predictor indicators by reducing their number to 5. A methodology for grouping students into three groups is proposed, using selected influencing indicators and predictor indicators in regression equations with the dependent variable of group number. Our study revealed that two body condition indicators and three body activity indicators have the greatest influence on men’s general health index. It was established that two body condition indicators have the greatest influence on women’s general health index. The determination of the most informative indicators is important for predicting health status and optimizing the health monitoring process. Full article
(This article belongs to the Special Issue Statistics and Probabilities and Their Role within Health Sciences)
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28 pages, 2135 KiB  
Article
Predicting Glycemic Control in a Small Cohort of Children with Type 1 Diabetes Using Machine Learning Algorithms
by Bogdan Neamtu, Mihai Octavian Negrea and Iuliana Neagu
Mathematics 2023, 11(20), 4388; https://doi.org/10.3390/math11204388 - 22 Oct 2023
Cited by 1 | Viewed by 1036
Abstract
Type 1 diabetes, a chronic condition characterized by insulin deficiency, is associated with various complications and reduced life expectancy and is increasing in global prevalence. Maintaining glycaemic control in children with type 1 diabetes, as reflected by glycated hemoglobin levels (A1C), is a [...] Read more.
Type 1 diabetes, a chronic condition characterized by insulin deficiency, is associated with various complications and reduced life expectancy and is increasing in global prevalence. Maintaining glycaemic control in children with type 1 diabetes, as reflected by glycated hemoglobin levels (A1C), is a challenging task. The American Association of Diabetes (ADA), the Pediatric Endocrine Society, and the International Diabetes Federation (ISPAD) recommend the adoption of a harmonized A1C of <7.5% across all pediatric groups. Our retrospective study included 79 children with type 1 diabetes and aimed to identify determinants pivotal to forecasting glycemic control, focusing on a single A1C cut-off value and exploring how machine learning algorithms can enhance clinical understanding, particularly with smaller sample sizes. Bivariate analysis identified correlations between glycemic control and disease duration, body mass index (BMI) Z-score at onset, A1C at onset above 7.5 g/dL, family income, living environment, maternal education level, episodes of ketoacidosis, and elevated cholesterol or triglyceride. Binary logistic regression stressed the association of ketoacidosis episodes (β = 21.1, p < 0.01) and elevated A1C levels at onset (β = 3.12, p < 0.01) and yielded an area under the receiver operating characteristic curve (AUROC) of 0.916. Two-step clustering emphasized socioeconomic factors, as well as disease complications and comorbidities, and delineated clusters based on these traits. The classification and regression tree (CART) yielded an AUROC of 0.954, slightly outperforming binary regression, providing a comprehensive view of interactions between disease characteristics, comorbidities, and socioeconomic status. Common to all methods were predictors regarding ketoacidosis episodes, the onset of A1C levels, and family income, signifying their overarching importance in glycaemic control. While logistic regression quantified risk, CART visually elucidated complex interactions and two-step clustering exposed patient subgroups that might require different intervention strategies, highlighting how the complementary nature of these analytical methods can enrich clinical interpretation. Full article
(This article belongs to the Special Issue Statistics and Probabilities and Their Role within Health Sciences)
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