Performance Evaluation of Cardiovascular Disease Prediction Using Supervised Machine Learning Algorithms

Cardiovascular disease has emerged as a very critical and intricate condition on a global scale. Healthcare practitioners have long faced substantial challenges in predicting and identifying heart disease. Medical and other healthcare facilities provide costly treatments and surgical procedures for cardiovascular conditions. People throughout the globe would benefit from early heart disease prediction since it would allow them to take action before the issue worsens. These days, heart disease is a major concern, and the primary causes of this illness include things like smoking, excessive alcohol use, and not getting enough exercise. Based on the extensive dataset that the healthcare industry has produced over time, machine learning has proven effective in decisions and forecasts. Logistic regression, Decision- Trees, Naive-Bayes, Random-Forest, and SVM are among the supervised machine learning algorithms that may anticipate the occurrence of heart illness. A synopsis of the algorithm’s performance assessment is also included. A comparison of five models showed that the Decision Tree and Random Forest models were better at predicting heart disease than the others. Also, these models were very accurate, with a 98.5% success rate.