An Intelligent and Scalable Framework for Early Heart Disease Detection Using Multimodal Health Data and Optimized Deep Learning Strategies

• Published in: SN computer science Vol. 6; no. 7; p. 765
• Main Authors: Alazzawi, Ammar K., Alharbi, Hasanain, Al-Khamees, Hussein A. A., Abdul Zahra, Musaddak Maher
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.10.2025; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Blood vessels; Cardiovascular disease; Cholesterol; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Coronary vessels; Data mining; Data Structures and Information Theory; Datasets; Deep learning; Discrete cosine transform; Electrocardiography; Fast Fourier transformations; Health care; Heart attacks; Heart diseases; Information Systems and Communication Service; Machine learning; Medical history; Multilayer perceptrons; Multilayers; Original Research; Pattern Recognition and Graphics; Software Engineering/Programming and Operating Systems; Vein & artery diseases; Vision; Feedforward neural network (FFNN); Region proposal network (RPN); Discrete cosine transform (DCT); Honey badger algorithm (HBA); Fast fourier transform (FFT); Multilayer perceptron–faster region-based convolutional neural network (MLP-FRCNN)
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-025-04288-4

Accurate methods for early detection are required since heart disease is still a major problem in world health. In order to accurately forecast the occurrence of heart illness using electrocardiogram data, this research introduces a hybrid model called MLP-FRCNN (Multi-Layer Perceptron-Faster Region-Based Convolutional Neural Network). The suggested method uses the DNLMS algorithm to remove baseline fluctuations and motion artifacts from ECG signals before processing them. By utilizing Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT), we are able to extract features, with a particular emphasis on important components such the QRS complex. To improve the Faster R-CNN, the Honey Badger Algorithm (HBA) takes into account factors including computing efficiency and overlapped detecting boxes. Results from further tests show that, in comparison to contemporary methods, we achieve better accuracy, sensitivity, specificity, and F1 score. Machine learning, which began with data modification and accumulation, has evolved into a powerful tool for driving transformative change and remains a central component of the ongoing pursuit of artificial intelligence. Accurate detection and treatment for coronary heart disease patients are greatly enhanced by the suggested model’s higher speed of convergence and enhanced predictive capabilities. To improve accuracy, an FFNN combiner takes the estimates from both the Faster R-CNN and MLP and applies them to patient’s demographics information and low-order characteristics. With a 98% accuracy rate, the hybrid model outperforms both MLP (94% accuracy rate) and HBA-FRCNN (96% accuracy rate).