Abstract: Cardiovascular diseases continue to be one of the leading causes of mortality worldwide, placing a significant burden on global healthcare systems. Early detection of heart disease plays a crucial role in reducing complications, improving survival rates, and enabling timely medical intervention.

With the advancement of computational intelligence, machine learning techniques have emerged as effective tools for analyzing medical datasets. These techniques are capable of identifying hidden patterns and relationships that may not be immediately visible through manual assessment.

In this study, a predictive framework is developed using the Random Forest algorithm to assess heart disease risk. The model processes structured patient health records containing both demographic and clinical attributes relevant to cardiovascular evaluation [6].

A systematic methodology was implemented, including data preprocessing, feature optimization, supervised model training, and validation. These steps were performed to ensure data consistency, improve model efficiency, and enhance predictive accuracy.

The ensemble learning mechanism underlying the Random Forest classifier combines multiple decision trees to produce stable and reliable predictions. This approach reduces overfitting, improves generalization performance, and enhances classification robustness.

Experimental evaluation using standard performance metrics demonstrates that the proposed system achieves consistent and dependable results. The developed framework has the potential to function as an effective clinical decision-support tool, assisting healthcare professionals in identifying high-risk patients and supporting early preventive care strategies [9].

Keywords: Cardiovascular Risk Prediction, Ensemble Learning, Random Forest Model, Clinical Data Analysis, Supervised Classification, Predictive Healthcare, Data-Driven Diagnosis, Intelligent Medical Systems

Downloads: | DOI: 10.17148/IARJSET.2026.13258