Classification of Cardiovascular Disease Information System Using Machine Learning Approaches

Recent advancements in computational approaches have facilitated the storage and collection of medical data for accurate medical diagnosis. Various computational techniques are used to improve the accuracy of disease diagnosis and reduce the diagnosis time, and the mortality rate. Advanced learning methods need to be used to improve efficacy and clinical significance. Machine learning methods are widely used in healthcare systems for screening, risk identification, prediction, and decision-making for different diseases. The sample sizes, features, location of data collection, performance metrics, and applied machine learning techniques play a significant role in the results of the machine learning-based cardiovascular disease data classification. This chapter discusses the performance of various machine learning algorithms relating to cardiovascular disease. The evaluation is done using different performance matrices like accuracy, precision, and recall. A comparative study of individual results of the models like support vector machine, K-nearest neighbor, Naïve Bayes, decision tree, random forest, and artificial neural network for predicting cardiovascular disease is carried out. It has been observed that random forest has a better accuracy of 0.92 when compared with other machine learning models.