Logistic Regression Model for Predicting Patient Outcomes - A Fusion of Mathematical Modelling and Machine Learning in the Health Sector

Aug 3, 2026

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Integration of mathematical modeling and machine learning in healthcare enables more accurate prediction of patient outcomes and supports evidence-driven responses to complex clinical challenges. The work focuses on logistic regression as a statistical foundation for binary outcome prediction, with MATLAB used to compute solutions from assumed patient data and visualize predictive performance. It also highlights machine learning’s role in chronic disease progression, clinical decision support via electronic health records, personalized medicine, risk stratification, medical imaging with deep learning, and resource optimization, while addressing data privacy and algorithm transparency.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Introduction

Mathematical modeling in healthcare

Role in public health decision-making

Use during health crises

Educational value and public trust

Synergy with machine learning

Types of mathematical modeling

Deterministic models

e-ISSN 2795-3278 p-ISSN 2795-3274 10.5281/zenodo.13954641 Vol. 07 Issue 08 August-2024 Manuscript ID: \#1576 Logistic Regression Model for Predicting Patient Outcomes: A Fusion of Mathematical Modelling and Machine Learning in the Health Sector Arivi, S.S1, Agbata, B.C2, Yahaya, D.J2, Abraham, S3, Shior, M.M4, Odo, C.E5, Saeed, O.B6 Amos,J7 1Department of Science & Education, Faculty of Education, Prince Abubakar Audu, University, Anyigba,Nigeria 2Department of Mathematics and Statistics, Faculty of Science, Confluence University of Science and Technology, Osara, Nigeria 3Department of Mathematics, School of Sciences, Federal College of Education (Technical), Ekiadolor, Nigeria 4Department of Mathematics/ Computer Science, Benue State University, Makurdi, Nigeria. 5Department of Mathematics, Federal Polytechnic Bida, Nigeria 6Department of Mathematics, Federal University of Technology, Minna, Niger State 7Department of Mathematics, Prince Abubakar Audu, University, Anyigba, Nigeria | | [Corresponding author](Corresponding author : abcinfotech08@gmail.com)[ : abcinfotech08@gmail.com](Corresponding author : abcinfotech08@gmail.com) | | | --- | --- | --- | | Abstract:
The integration of Mathematical Modeling and Machine Learning in the health sector has led to significant advancements in predicting patient outcomes and addressing healthcare challenges. This paper explores various methodologies, including logistic regression model, which serves as a robust statistical tool for predicting binary health outcomes. MATLAB is employed to obtain solutions for the logistic regression model by assuming patient data, and the graphical results demonstrate the model's effectiveness in predicting patient outcomes. Furthermore, machine learning techniques have emerged as vital for modeling disease progression in chronic conditions, enabling personalized treatment plans through analysis of historical patient data. Other areas explored include Clinical Decision Support Systems (CDSS) that leverage machine learning algorithms to enhance clinical decision-making by analyzing electronic health records and providing evidencebased recommendations, as well as personalized medicine, medical imaging analysis using deep learning, patient risk stratification, and healthcare resource optimization. The novelty of this work lies in its comprehensive examination of the interconnected roles of mathematical modeling and machine learning across various facets of healthcare, offering insights into how these technologies can be effectively integrated to improve patient care and outcomes. By addressing the challenges associated with data privacy and algorithm transparency, this paper highlights the transformative potential of machine learning in enhancing predictive analytics within the health sector.
Keywords:
Mathematical Modeling, Machine Learning, Logistic Regression, Patient Outcomes, Epidemic Forecasting | | | This work is licensed under Creative Commons Attribution 4.0 License. Page 10 of 28 1. Introduction Mathematical modeling has become an essential tool in the health sector, it facilitates a deeper understanding of complex biological processes and disease dynamics. By translating real-world phenomena into mathematical representations, researchers can simulate scenarios, predict outcomes, and evaluate the potential impact of various interventions (Meyers et al., 2005, Odeh et al, 2024). The application of mathematical models allows for the examination of disease transmission patterns, resource allocation, and the effectiveness of public health strategies, making it a cornerstone of epidemiological studies (Keeling & Rohani, 2008) . One of the most significant contributions of mathematical modeling in public health is its ability to inform decision-making processes. For instance, models can project the spread of infectious diseases, helping health officials anticipate outbreaks and allocate resources accordingly (

FAQs

How does mathematical modeling support patient-outcome prediction in healthcare?

Mathematical modeling translates real-world health phenomena into quantitative representations, enabling simulations, outcome prediction, and evaluation of intervention impacts. It also helps examine transmission patterns and resource allocation for public health planning.

What modeling approach does the paper emphasize for binary patient outcomes?

The paper emphasizes logistic regression as a robust statistical tool for predicting binary health outcomes. Solutions are obtained using MATLAB under assumed patient data and assessed through graphical results.

Which healthcare applications beyond logistic regression are discussed?

The paper discusses machine learning for chronic disease progression and personalized treatment, clinical decision support systems using electronic health records, personalized medicine, deep-learning-based medical imaging analysis, patient risk stratification, and healthcare resource optimization.

Aug 3, 2026

Upload by Quinn