Cardiovascular diseases (CVDs) remain a leading cause of global morbidity and mortality. Early identification of individuals at risk of heart disease is crucial for effective preventive interventions. To improve the prediction accuracy, this paper proposed Heart Disease Prediction using the Density-Based Ordering of Clustering Objects (DBOCO) framework. The Dataset has been pre-processed using Weighted Transform K-Means Clustering (WTKMC). Features are selected using Ensemble Feature Selection (EFS) with a Weighted Binary Bat Algorithm (WBBAT) used to ensure that the emphasis is on the most relevant predictors. Finally, the prediction has been done using the Density-Based Ordering of Clustering method, which has been designed exclusively for cardiovascular disease prediction. DBOCO, a density-based clustering approach, effectively finds dense clusters within data, allowing for the inherent overlap in cardiovascular risk variables. DBOCO captures complicated patterns by detecting these overlapping clusters, improving the accuracy of disease prediction models. The proposed approach has been verified with heart disease datasets, displaying higher performance than traditional methods. This study marks a substantial leap in predicting cardiovascular disease providing a comprehensive and dependable framework for early identification and preventive concern.