What question did this study set out to answer?

The aim is to compare traditional machine learning algorithms with neural networks for heart disease prediction.

January 10, 20260 citationsOpen Access

A Comparative Analysis of Traditional and Neural Network-Based Models for Heart Disease Prediction

PGP Vidya Sagar Sakinala GopalKoneru Lakshmaiah Education Foundation

Key Result

Artificial Neural Networks outperformed traditional classifiers in predicting heart disease, improving precision, recall, and overall predictive accuracy.

Key Points

The aim is to compare traditional machine learning algorithms with neural networks for heart disease prediction.
Conducted comparative analysis of SVM, KNN, and ANN models.
Utilized clinical indicators such as type of chest pain and blood pressure.
Analyzed predictive performance metrics including precision, recall, and F1-score.
ANN outperformed SVM and KNN in predictive accuracy metrics.
ANN demonstrated better precision, recall, and F1-score compared to traditional classifiers.
Findings suggest ANN enhances diagnostic support in cardiovascular care.

Structured PICO

Does an Artificial Neural Network (ANN) improve predictive accuracy for heart disease compared to traditional machine learning models (SVM, KNN)?

Population

Patient medical records containing clinical indicators including age, gender, type of chest pain, resting blood pressure, cholesterol, fasting blood sugar, maximum heart rate, and electrocardiographic findings

Intervention

Artificial Neural Network (ANN) model

Comparator

Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) models

Outcome

Predictive accuracy and Area Under the ROC Curve (AUC) for heart disease detection

Artificial Neural Networks provide superior accuracy and robustness in predicting heart disease from clinical data compared to traditional machine learning algorithms.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

ABSTRACTHeart disease is the most prevalent cause of morbidity and mortality all overthe world hence the importance of precise and timely means of predicting thedisease. The research paper provides comparative analysis of machine learningalgorithms-Support Vector Machine (SVM), k- Nearest Neighbor (KNN) andArtificial Neural Networks ( ANN ) to build an early detection model of heartdisease on the basis of some of the most vital clinical indicators, i.e. type ofchest pain, blood pressure, cholesterol level and electrocardiographic findings.The proposed framework aims to identify individuals at elevated cardiovascularrisk, thereby enabling earlier intervention and reducing adverse outcomes.Initial models developed using SVM and KNN demonstrated improvedpredictive performance over traditional classifiers such as Na ̈ıve Bayes.However, to address the limitations of linear decision bound- aries and locallearning, an ANN-based model was introduced to capture com- plex nonlinearrelationships within the data. The comparison of the experimental resultsshowed that ANN has a better performance than the other models in manyaspects, such as precision of prediction, recall or true positive rate, and f1-score as well as area under a ROC curve, and ANN is robust and reliable asdiagnostic support. The findings suggest that incorporating deep learning intoclinical decision-making tools can significantly enhance predictive accuracy andcontribute to improved patient outcomes in cardiovascular care.

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