A Comparative Analysis of Machine Learning Algorithms for the Early Prediction of Cardiovascular Disease

Early diagnosis of cardiovascular disease signs and continued medical therapy can lower death rates and the number of afflicted people. As there are more occurrences of cardiovascular disease, more patients are waiting to be diagnosed, which makes the diagnosis process inefficient in terms of the amount of labor time needed. However, if a prediction system is in place, this issue may be resolved. Several algorithms may be used to solve the current issue. A comparative analysis of the algorithms of machine learning for the detection of cardiovascular disease would involve evaluating and comparing the performance of different algorithms on a dataset of cardiovascular disease patients. Common algorithms that may be used in such an analysis include logistic regression, random forests, Naïve Bayes, and support vector machines. The selection of the algorithm will depend on the specific characteristics of the dataset and the research question being addressed. The Dataset will be collected from Kaggle.