학술논문
Heart Disease Prediction Model using various Supervised Learning Algorithm
Document Type
Conference
Source
2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT) Communication Systems and Network Technologies (CSNT), 2023 IEEE 12th International Conference on. :197-201 Apr, 2023
Subject
Language
ISSN
2473-5655
Abstract
A computer can learn from data and improve its predictive abilities without requiring human interaction through artificial intelligence (AI) derived from machine learning (ML). Machine learning’s core concept is to replicate how the brain functions. ML approaches have proven to be effective predictors in a variety of application fields, including healthcare and medicine. In this study, supervised machine learning approaches for predicting cardiac disorders were analyzed and compared using medical records from the UCI Machine Learning repository. This research examines the effectiveness of various models, including K-Nearest Neighbor (KNN), Logistic Regression (LR) models and Support Vector Machines (SVM), and The (Area Under ROC Curve) AUC score was used to evaluate the effectiveness of various algorithms. AUC is a measurement. In order to evaluate the effectiveness of various algorithms, we used the (Area Under ROC Curve) AUC score. The choice to adopt a machine learning algorithm is made if the AUC score is more than 0.5, which is an evaluation metric that aids in validating how effective the algorithm is. AUC scores for logistic regression are the highest of all ML algorithms in the trial (0.87).