Heart Disease Prediction: Optimization of Machine Learning Algorithms

Cardiovascular ailments have become a principal cause of mortality globally in recent decades. It then goes on to discuss the use of big data and machine learning algorithms for predictive analysis within the medical field. The five different algorithms discussed are Logistic Regression (LR), Support Vector Machines (SVM), K-Nearest Neighbour (KNN), Decision Tree (DT) and Random Forest (RF). Finally, it states that out these methods Decision Tree was found to be most accurate when predicting potential cases of cardiac illness. The practical implications of this paper are that it could help medical professionals make more informed decisions about patient care. By using machine learning algorithms to analyse large volumes of data, clinicians can better predict the likelihood of an individual developing heart disease and take appropriate action accordingly. This research also provides insight into how big data can be used for predictive analysis in healthcare settings, which could lead to improved outcomes for patients with cardiac illness.