Heart Disease Diagnosis by Machine Learning Techniques

Abstract

Disease diagnosis is a crucial component of healthcare for early disease detection and treatment. Recent machine learning techniques have improved efficiency and accuracy due to the abundance of available medical data. In this study, various algorithms for diagnosing heart disease are examined, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naive Bayes (NB), Ensemble Learning and Support Vector Machine (SVM). This study’s findings will advance disease diagnosis methods and build a more efficient healthcare system. Medical professionals can use machine learning to diagnose diseases, provide better patient care, and save lives.