Cardiovascular Disease Risk Assessment using Machine Learning

Abstract

Cardiovascular diseases (CVD) are one of the highest causes of death in the world. The early detection of cardiac risk is a critical factor in proper diagnosis and treatment. This way, patients with critical needs get priority access to doctors and healthcare systerns. In this study, a cardiac risk assessment system was developed using the Logistic Regression algorithm, a machine learning model that has a high accuracy and is easy to interpret. The datasetused in this study included information from various patients. A total of 13 features were used to train the Logistic Regression model, including age, gender, blood pressure, and cholesterol levels. The results demonstrated that the Logistic Regression algorithm achieved high accuracy in predicting CVD risk, with an accuracy of 86.89. The main challenge when it comes to CVD risk assessment is the complexity of algorithms which makes it difficult for healthcare practitioners to interpret the results. Some systems require the personnel to go through additional training to use the risk assessment system, which can be time consuming. Logistic Regression is straightforward and simple. It is easy to interpret, making it suitable for clinical settings. It also has a well-established framework, which makes it very practical and reliable. This study showcases the importance of machine learning in the field of healthcare and highlights the effectiveness of the Logistic Regression algorithm in predicting cardiac risk. The high accuracy achieved by the model enables the early identification of cardiovascular disease risk. This makes it a useful tool for the healthcare industry and public health initiatives.