Explore the relationship among various factors: Analysis of logistic regression

Abstract

Heart disease is a common condition among living beings, especially humans. It can lead to severe consequences, including death. Therefore, it is essential to take preventive measures. Early detection can significantly increase the survival rate and reduce suffering. This study aims to demonstrate the application of logistic regression using heart disease as a case study. First, the paper introduces heart disease, including its causes, risk groups, contributing factors, current status, and available treatments. Next, it presents the relevant variables and pre-diction methods used in the logistic regression model. The results are then discussed, showing clear relationships between the predictors and the presence of heart disease. In conclusion, the study uses visual charts and statistical analysis to illustrate the value of logistic regression in medical diagnostics. According to the findings, logistic regression can effectively support the identification of disease risks and contribute to early diagnosis efforts. Future research should focus on optimizing model performance, integrating additional variables, and combining logistic regression with other advanced machine learning techniques to enhance diagnostic accuracy and reliability further.