摘要：One of the most difficult challenges today facing medical practitioners is determining whether or not a person may acquire heart disease. Heart disease is the greatest cause of death in the contemporary period, killing about one person every minute on average. One of the most important uses of data science is the analysis of massive amounts of data generated in the area of healthcare. Since anticipating cardiac sickness is difficult, there is an urgent need to automate the procedure. This will assist to limit the hazards of the treatment and provide patients with early warnings. By using the Principal Component Analysis (PCA) dimensionality reduction approach to datasets of cardiovascular disorders, this research analyses the overall performance of a variety of different machine learning models. Furthermore, the authors use the K Nearest Neighbor Model and the Random Forest Model to the datasets and compare the model's performance with and without PCA. This is done in order to identify which approach yields the greatest outcomes. In compared to the KNN approach, the Random Forest (RF) algorithm in combination with the Principal Component Analysis (PCA) achieved a score of 91.0 percent in the categorization of heart disease.
