This paper proposes a novel hybrid machine learning method, Hybrid Random Forest with Linear Model (HRFLM), for effective heart disease prediction. The method aims to identify significant features and improve the accuracy of cardiovascular disease prediction. The proposed model uses a combination of features and various classification techniques, achieving an accuracy of 88.7% in predicting heart disease. The study discusses the use of machine learning techniques in predicting heart disease, including decision trees, support vector machines, and neural networks. The paper also presents the results of experiments using the Cleveland heart dataset, which is a well-known dataset for heart disease prediction. The results show that the HRFLM method outperforms existing methods in terms of accuracy and performance. The study also discusses the use of data mining techniques in the medical field and the importance of feature selection in improving the accuracy of heart disease prediction. The paper concludes that the proposed HRFLM method is effective in predicting heart disease and has the potential to be used in future research and applications.This paper proposes a novel hybrid machine learning method, Hybrid Random Forest with Linear Model (HRFLM), for effective heart disease prediction. The method aims to identify significant features and improve the accuracy of cardiovascular disease prediction. The proposed model uses a combination of features and various classification techniques, achieving an accuracy of 88.7% in predicting heart disease. The study discusses the use of machine learning techniques in predicting heart disease, including decision trees, support vector machines, and neural networks. The paper also presents the results of experiments using the Cleveland heart dataset, which is a well-known dataset for heart disease prediction. The results show that the HRFLM method outperforms existing methods in terms of accuracy and performance. The study also discusses the use of data mining techniques in the medical field and the importance of feature selection in improving the accuracy of heart disease prediction. The paper concludes that the proposed HRFLM method is effective in predicting heart disease and has the potential to be used in future research and applications.