Predictive modeling of cardiovascular disorder: a comparison of regression model and random forest

The growing prevalence of cardiovascular disorders (CVDs) has underlined the need for an accurate predictive approach to enhance earlier detection and treatment of the disorder. This study provides a comparison of two main machine learning techniques used for the prediction of cardiovascular diseases namely: Logistic Regression and Random Forest. Logistic Regression, a classical statistical method, was initially evaluated for its simplicity and data-interpretability. In contrast, Random Forests, an ensemble-based learning algorithm, is tested to evaluate the extent to which it can manually handle (deal with) the complex, non-linear relationships that may occur in the datasets. The main objective of the research is to evaluate the risk-associated strategy from several different standpoints. The study used a large dataset that consisted of many different medical and lifestyle-related parameters, which were proven to be major contributors to cardiovascular health risks. Consequently, through a deep study, the most suitable of the two can be early CVD risk prediction; this is what healthcare decision makers strive to assist them in prevention and treatment techniques. These findings clearly suggest the use of advanced predictive analytics as an effective tool to reduce the global burden of cardiovascular diseases in the healthcare sector.