EARLY DIAGNOSIS OF CARDIOVASCULAR DISEASE USING LOGISTIC REGRESSION

Cardiovascular disease is one of the most life-threatening diseases and accounts for nearly 17 million deaths worldwide. Early diagnosis plays a crucial role in providing timely treatment and reducing mortality. Several machine learning and deep learning techniques are available to classify the presence or absence of cardiovascular disease. In this study, a Logistic Regression (LR) model is applied to the UCI dataset for heart disease classification. To enhance the model performance, data preprocessing steps such as dataset cleaning, missing value handling, and feature selection were carried out. Feature selection was performed based on the correlation between each feature and the target variable, and highly positively correlated features were selected. The dataset was then divided into training and testing sets using different split ratios: 90:10, 80:20, 70:30, 50:50, and 40:60. Among these, the 90:10 split ratio achieved the best performance. The proposed Logistic Regression model attained an accuracy of 87.10%.