Machine Learning for Personalized Risk Assessment and Early Detection of Heart Disease

Abstract

Abstract: Today in our busy life it very difficult for us to look after our health. Cardio vascular diseases are very common now days which cause loss of billions of lives world-wide. Our lifestyles have a major impact on our health causing various chronic diseases. Machine learning (ML) can revolutionize the field of cardiovascular disease prediction by providing more accurate, nuanced, and personalized risk assessment, leading to improved patient health. A properly trained machine learning model can easily detect or predict the heart disease. Within the context of using artificial intelligence for forecasting and assessing the occurrence of heart ailments, here are various factors that contribute to the heart disease to be considered, such as lifestyle choices and medical conditions to predict whether the person is at high risk or not. ML technology can also analyze large datasets of patient data to identify patterns and risk factors that cannot be easily distinguished, predicted, or detected using traditional methods. Estimators like k-NN classifier, Decision tree classifier, Gradient boosting classifier and Gaussian Naive bayes (NB) classifier, are provided with different characteristics or feature extracted from the dataset, demonstrated consistent and reliable performance in predicting heart disease. The utilization of these attributes can potentially aid the medical field for on time detection and diagnosis of the heart disease.