The Uses of Multiple Linear Regression as A Predictive Model for Factors of Breast Cancer in Malaysia
Year: 2024
Type: article
Study design: Observational Study
Abstract: The application of multiple linear regression analysis has significantly increased in popularity among researchers, becoming a predominant model for the analysis of data associated with complex phenomena. This study specifically focuses on the prediction of breast cancer symptoms using linear regression. Data was collected from 569 breast cancer patients who received treatment at general hospital in Malaysia with the secondary data being recorded by nurses and doctors. To ascertain the factors influencing breast cancer, a research was conducted at general hospital in Malaysia, which examined four independent variables, each with various combinations of variable types. The primary aim of this study was to identify the factors that significantly influence breast cancer factors at general hospital. All collected data will be analyzed employing the Statistical Package for Social Science (SPSS) software method. The analysis will include tests for data normality and the calculation of coefficients to meet the study's objectives. The findings indicate that breast cancer severity is significantly influenced by radius, as determined through multiple linear regression analysis. The conclusion of this chapter will present a comprehensive summary of the research findings, as well as acknowledge the limitations of the study. Furthermore, recommendations for enhancing another predicting modeling techniques in future breast cancer research are included.
Authors: Universiti Tun Hussein Onn Malaysia, Muhammad Ammar Shafi, Zulfana Lidinillah, Universiti Tun Hussein Onn Malaysia, Shawal Iskandar Sabri, Universiti Tun Hussein Onn Malaysia, Nor Faezan Abdul Rashid, Universiti Teknologi MARA, Mohd Saifullah Rusiman, Universiti Tun Hussein Onn Malaysia Edu Hub Pagoh, Nur Ain Ebas, Universiti Tun Hussein Onn Malaysia
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4406724979