Advanced Machine Learning Approaches for Heart DiseasePrediction and Prevention with Comparative Analysis ofClassification Algorithms

Technology has been used more and more in recent years to help avoid awide range of illnesses. Heart problems are among the deadliest of them,resulting from a variety of causative variables. However, the start ofcardiac disease can be predicted and prevented with the appropriateknowledge and methods. In this work, we conduct a thoroughinvestigation of methods designed to categorize cardiac disease using adataset assembled in ۱۹۸۸. We use a variety of machine learningalgorithms, such as decision trees, naïve Bayes, logistic regression,random forests, support vector machines, extreme gradient boost, and knearestneighbors, to do this. We determine the individual performancesof these algorithms and determine which ones perform best by rigorouslyevaluating them. Additionally, we explore relevant debates regardingthese algorithms' operational preparedness for practical use. Our goal indoing this research is to make a small but meaningful contribution to thecurrent attempts to use technology to prevent heart disease and otherpreventive healthcare initiatives. Our results provide insight into thepractical factors necessary for the use of machine learning techniques inclinical settings, in addition to illuminating the effectiveness of differentapproaches in disease classification.

کلیدواژه ها:

Illness Classification ، Artificial Intelligence ، Practical MachineLearning