Logistic Regression Model in Predicting Stroke

Yip, Xue Ni (2023) Logistic Regression Model in Predicting Stroke. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

In this final year project, we investigate the stroke problems using Logistic Regression. There are five chapters in the report. In Chapter 1, we provide information on the forms of stroke, their prevalence worldwide and the proportion of individuals who die from stroke. In Section 1 of Chapter 1, we provide some terms and definitions pertaining to the subjects of stroke and Logistic Regression. In Section 2 of Chapter 1, we present the problem statement, objectives and scope of doing this analysis on stroke. In Chapter 2, we refer to some summary conclusions from the other research papers. Some literature review related to stroke and logistic regression is presented. In Chapter 3, we study the methodologies of the logistic regression model. In Section 1 of Chapter 3, we study the logistic regression model, the assumptions of the logistic regression model and software tool that is used to generate results which is IBM SPSS Statistics 27. In Section 2 of Chapter 3, we present the data requirements and data collection, data pre-processing, label encoding and data cleaning. In Chapter 4, we explain the main results of the whole dataset that generated by SPSS. In Section 1 of Chapter 4, we checked the assumptions of logistic regression before running the data. In Section 2 of Chapter 4, the main results were generated by SPSS software and we did the interpretations for each of the result. We attempted to remove the insignificant predictor variables which are not significant contribute to the model until all the significant predictor variables remained in the model. In Chapter 5, we present the conclusion of the results as well as some future work and ideas for improvements. Keywords: Logistic Regression, Stroke Prediction, Odds Ratio, Wald Test, Hosmer and Lemeshow Test, SPSS