Diagnosis of Heart Disease using Machine Learning Algorithms

Abstract

The diagnosis of heart disease using machine learning algorithms is a promising field that has the potential to improve the accuracy and efficiency of diagnoses. This paper discusses four machine learning algorithms, including Support Vector Machines (SVM), Decision Trees, and Logistic Regression, that can be used for heart disease diagnosis. These algorithms work by identifying the most relevant features related to heart disease, pre-processing the dataset, and fine-tuning the models for optimal performance. Once well-performing models are obtained, they can be deployed for real-time diagnosis. The use of machine learning algorithms for heart disease diagnosis can provide faster, more accurate, and more efficient diagnoses, leading to better patient outcomes and reduced healthcare costs. However, careful hyper parameter tuning and dataset preprocessing are required to ensure optimal performance. Overall, the use of machine learning algorithms for heart disease diagnosis shows great promise and has the potential to revolutionize the way heart disease is diagnosed and treated