Abstract

Predictive modelling, especially the use of horizontal lines, has become an important tool in clinical practice to help make informed decisions and accurate predictions. This study focuses on the use of horizontal regression in clinical practice, evaluating its effectiveness in revealing patterns and improving the accuracy of predictions. This study introduces the process of developing a linear model, emphasizing the importance of preliminary data analysis, feature selection, and model evaluation. To make sure your model's predictions are accurate, consider key assumptions such as sampling, independence, and homoscedasticity. The main goal is to provide doctors with the knowledge and skills needed to make accurate predictions using horizontal models, thus improving the clinical decision-making process. This study explores the fundamentals of linear regression, evaluates its suitability for various clinical applications, and outlines the important steps in building a good model. The aim is to provide doctors with the knowledge and skills that will enable them to make informed decisions using technology. Issues such as multicollinearity and overfitting are addressed, while the importance of engineering design and variable selection to optimize model performance is further explored. This research contributes to the nonstop advancement of data-driven decision-making in healthcare by highlighting the imperative part of the even pivot in making exact expectations. Experiences from this research have the potential to progress quiet results, progress asset allotment, and actualize evidence-based practices within the healthcare industry.

Library of Congress Subject Headings

Medical care--Data processing; Mathematical models; Health services administration; Regression analysis

Publication Date

2024

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ioannis Karamitsos

Campus

RIT Dubai

Plan Codes

PROFST-MS

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