Improving Cardiac Disease Prognosis Through Machine Learning: An Extensive Examination of Algorithms and Predictive Models

The heart plays a vital role as the central organ in the human body, responsible for supplying oxygen and nutrients to all other organs through the blood circulation. However, any malfunction in its functioning can lead to severe consequences. One of the examples is cardiac arrest. The early prediction of cardiac issues poses a significant challenge in the medical field. By harnessing various machine learning techniques, it becomes feasible to make predictions based on additional health-related data, thereby facilitating the identification and prevention of heart-related problems. With the abundance of patient data available, these predictive models can assist in forecasting future disease outbreaks. Machine learning algorithms, such as Decision Trees, Logistic Regression, K-Nearest Neighbors, Support Vector Machines, and others, have been extensively utilized in the prognosis of cardiac diseases, enabling more accurate predictions.