A Deep Learning-Based Framework for Intelligent Heart Disease Prediction Using ANN and CNN

Author(s)	Ms. Sanjana Bala J, Mr. Deepu K, Ms. Ayesha Parveen, Mr. Akshay G S, Ms. Rakshitha J N
Country	India
Abstract	This paper presents a comparative studyof machine learning and deep learning models for heartdisease prediction using clinical datasets. Exploratorydata analysis identified key physiological featuresstrongly associated with cardiovascular risk. ArtificialNeural Network (ANN) and Convolutional Neural Network (CNN) models were developed and evaluatedusing standard performance metrics. Experimentalresults demonstrate that CNN models outperform ANNmodels in terms of accuracy, generalization, androbustness to class imbalance. The CNN achieved anoverall accuracy of approximately 98%, effectivelycapturing complex non-linear patterns in biomedicaldata. These findings highlight the potential of CNNbased models for reliable and automated heart diseaseclassification in clinical decision support systems.
Keywords	Congenital Heart Disease, Pediatric Heart Sounds, Phonocardiogram (PCG), ZCH Sound Dataset, Machine Learning, Deep Learning, Signal Processing, Feature Extraction, Heart Sound Classification, ANN, CNN.
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-24
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.84242

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