Application of Machine Learning Algorithms for Automatic Detection of Risk in Heart Disease

Abstract

The main cause of death worldwide is heart disease, emphasizing the need for accurate risk prediction models to recognise those who are at high risk. In this study, we propose an automated approach using artificial intelligence to forecast the likelihood of developing heart disease. The dataset consists of various clinical and demographic features, including blood pressure, cholesterol levels, age, gender, and exercise habits. We evaluate the performance of numerous machine learning algorithms, such as neural networks, logistic regression, support vector machines, and random forests, in predicting the likelihood of heart disease. Our results demonstrate that automated learning techniques can effectively determine the likelihood of cardiac disease with high accuracy, precision, and recall. Furthermore, we conduct analysis feature importance to the risk prediction model can determine which factors have the greatest impact. The automated risk prediction system can provide early detection and intervention strategies for individuals at high risk, enabling proactive healthcare management and reducing the burden of heart disease. This research showcases the potential of machine learning algorithms in improving heart disease risk assessment and guiding personalized preventive measures. Machine learning which is employed in worldwide in different industries. In the healthcare industry, there are no exceptions. Determining whether or not there will be heart problems, abnormalities and other disorders can be highly dependent on machine learning. For the purpose of predicting probable heart conditions in humans, we are creating machine learning algorithms. In our work, We contrast the effectiveness of several classifiers, which includs Naive Bayes, Decision Tree, Logistic Regression, Random Forest, SVM. Finally, we evaluate the effectiveness of the suggested classifiers, including the more accurate Ada-boost and XG-boost. Early detection of heart disease in high-risk persons is essential for assisting them in deciding whether to alter their lifestyle, which reduces consequences. Medical data's hidden patterns may be exploited to diagnose health issues.