A novel machine learning approach using Support Vector Machine (SVM) effectively predicted premature cardiovascular disease and outperformed other machine learning techniques.
Does a Support Vector Machine (SVM) model improve the prediction of premature cardiovascular disease compared to other machine learning techniques?
A novel Support Vector Machine model demonstrates potential for predicting premature cardiovascular disease based on symptoms and medical observations.
Cardio Vascular disease (CVD), involving the heart and blood vessels is one of the most leading causes of death throughout the world. There are several risk factors for causing heart diseases like sedentary lifestyle, unhealthy diet, obesity, diabetes, hypertension, smoking and consumption of alcohol, stress, hereditary factory etc. Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being. At present, the highly stressful life with bad lifestyle activities causes heart disease at a very young age. The main aim of this research is to predict the premature heart disease based on machine learning algorithms. This paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage itself. Support Vector Machine (SVM) is used for segregating the CVD patients based on their symptoms and medical observation. The experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time. A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques.
Kim et al. (Wed,) conducted a other in Cardiovascular disease. Support Vector Machine (SVM) vs. Other machine learning techniques was evaluated on Prediction of premature heart disease. A novel machine learning approach using Support Vector Machine (SVM) effectively predicted premature cardiovascular disease and outperformed other machine learning techniques.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: