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

P Vidya Sagar Sakinala Gopal

doi:10.5281/zenodo.18161691

January 6, 2026Open Access

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

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Key result

Artificial neural networks outperform traditional classifiers in predicting heart disease with improved precision and accuracy.

Why the study?

Heart disease is the most prevalent cause of morbidity and mortality worldwide, underscoring the need for precise and timely prediction methods.

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

Key Points

To evaluate and compare the predictive accuracy of Support Vector Machines, k-Nearest Neighbor, and Artificial Neural Networks for early heart disease detection using vital clinical parameters.
Analyzed clinical indicators including chest pain type, blood pressure, cholesterol levels, and electrocardiographic findings to predict cardiovascular risk.
Developed and evaluated classification models using Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Naïve Bayes, and Artificial Neural Networks (ANN).
Assessed model performance across data preprocessing, model design, training, and validation using precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve.
Initial models constructed using SVM and KNN demonstrated superior predictive performance compared with baseline traditional classifiers such as Naïve Bayes.
The ANN-based model outperformed SVM and KNN across all primary evaluation metrics—including precision, recall (true positive rate), F1-score, and area under the ROC curve—by modeling complex nonlinear relationships in biomedical data.

Structured PICO

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

P

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

I

Intervention

Artificial Neural Network (ANN) model

C

Comparator

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

O

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.

Implication

ANN may aid heart disease prediction accuracy; hypothesis-generating and requires prospective validation before clinical use.

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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Cite This Study

P Vidya Sagar Sakinala Gopal (2026) studied this question. Artificial Neural Networks outperformed traditional classifiers in predicting heart disease, improving precision, recall, and overall predictive accuracy.

synapsesocial.com/papers/696321d091e05aa366cb814a https://doi.org/10.5281/zenodo.18161691