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

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 evidence-based 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.