About this document

ML Models for Heart Disease Prediction by Fayaz Ahamed Shaik is a document available to read on EtoBox.

The document presents a study on an advanced machine learning architecture for detecting, predicting, and classifying heart diseases using a dataset of 303 patient records. Four machine learning algorithms—Logistic Regression, Random Forest, Support Vector Machines, and Neural Networks—were evaluated, with SVM showing the highest stability and accuracy for clinical use despite Neural Networks achieving the highest accuracy overall. The research emphasizes the potential of machine learning to enhance early d

Author

Fayaz Ahamed Shaik

Language

EN