Heart Disease Prediction using Random Forest Classifier

Abstract

Because cardiovascular disease is the worldwide top cause of death, early identification and precise prediction are crucial for successful prevention and treatments. Machine learning algorithms have showed promise, with the potential to enhance risk assessment and guide therapy decisions. Age and exercise habits are all parameters included by the study. This method is well-suited for this task because of the capacity to manage missing data and find complex, non-linear relationships between variables. By analyzing the relevance of various features, the study also contributes to a improved awareness of the risk factors connected with heart disease. This work emphasis the necessity of employing machine learning technologies in healthcare to improve early detection and assessment of risk in heart disease, resulting in better patient care and decreased mortality rates. The findings indicate that the algorithm Random Forest is a trustworthy and accurate technique for predicting cardiac disease. This work contributes to ongoing efforts to develop efficient, non-invasive, and accessible methods for detecting early heart illness.