Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary
Parastoo Golpour1, Majid Ghayour-Mobarhan2,3, Azadeh Saki1
1Department of Epidemiology and Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad 917791-8564, Iran.
Insights
Machine learning models can predict the need for coronary angiography. Naïve Bayes, using gender, age, and fasting blood glucose, demonstrated effective prediction, supporting clinical decision-making.
Area of Science:
- Cardiology
- Artificial Intelligence
- Machine Learning
Background:
- Coronary angiography is the gold standard for diagnosing cardiovascular disease but is invasive.
- Predictive methods are needed to determine the necessity of angiography, reducing risks.
- Machine learning (ML) offers data-driven approaches for pattern recognition in medical diagnostics.
Purpose of the Study:
- To compare the predictive performance of Support Vector Machine (SVM), Naïve Bayes, and logistic regression models.
- To identify key diagnostic factors for predicting the need for coronary angiography.
- To evaluate ML algorithms for supporting clinical decisions in cardiology.
Main Methods:
- A cross-sectional study involving 1187 angiography candidates.
- Application of logistic regression, Naïve Bayes, and SVM models to predict angiography outcomes.
- Comparative analysis of model performance using sensitivity, specificity, AUC, and accuracy.
Main Results:
- All models showed similar predictive power, with Area Under the Curve (AUC) values around 0.75.
- The Naïve Bayes model achieved the best performance with only three variables (gender, age, fasting blood glucose).
- Logistic regression used seven variables, and SVM used six, highlighting Naïve Bayes' parsimony.
Conclusions:
- Gender, age, and fasting blood glucose are critical predictors for coronary angiography necessity.
- The Naïve Bayes model effectively utilizes these key factors for prediction.
- These ML models offer pragmatic, cost-effective tools to aid physicians in angiography decision-making.
Abstract:
(1) Background: Coronary angiography is considered to be the most reliable method for the diagnosis of cardiovascular disease. However, angiography is an invasive procedure that carries a risk of complications; hence, it would be preferable for an appropriate method to be applied to determine the necessity for angiography. The objective of this study was to compare support vector machine, naïve Bayes and logistic regressions to determine the diagnostic factors that can predict the need for coronary angiography. These models are machine learning algorithms. Machine learning is considered to be a branch of artificial intelligence. Its aims are to design and develop algorithms that allow computers to improve their performance on data analysis and decision making. The process involves the analysis of past experiences to find practical and helpful regularities and patterns, which may also be overlooked by a human. (2) Materials and Methods: This cross-sectional study was performed on 1187 candidates for angiography referred to Ghaem Hospital, Mashhad, Iran from 2011 to 2012. A logistic regression, naive Bayes and support vector machine were applied to determine whether they could predict the results of angiography. Afterwards, the sensitivity, specificity, positive and negative predictive values, AUC (area under the curve) and accuracy of all three models were computed in order to compare them. All analyses were performed using R 3.4.3 software (R Core Team; Auckland, New Zealand) with the help of other software packages including receiver operating characteristic (ROC), caret, e1071 and rminer. (3) Results: The area under the curve for logistic regression, naïve Bayes and support vector machine were similar-0.76, 0.74 and 0.75, respectively. Thus, in terms of the model parsimony and simplicity of application, the naïve Bayes model with three variables had the best performance in comparison with the logistic regression model with seven variables and support vector machine with six variables. (4) Conclusions: Gender, age and fasting blood glucose (FBG) were found to be the most important factors to predict the result of coronary angiography. The naïve Bayes model performed well using these three variables alone, and they are considered important variables for the other two models as well. According to an acceptable prediction of the models, they can be used as pragmatic, cost-effective and valuable methods that support physicians in decision making.
More Related Videos
Related Concept Videos
01:20Comparing the Survival Analysis of Two or More Groups
01:27Coronary Artery Disease V: Interprofessional Care
01:15Receiver Operating Characteristic Plot
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
01:13Sensitivity, Specificity, and Predicted Value
Sensitivity is the...