• Raj Narule
• Bhagyashri Dongare
• Deepak Yadav
• Monali Gajbhiye

Cardiovascular disease (CVD) prediction from electrocardiogram (ECG) images is a crucial advancement in digital healthcare, enabling rapid and automated diagnosis without requiring immediate doctor consultations. This system leverages digital ECG images, which are secure, easily stored, transmitted, and retrieved, to facilitate real-time analysis through machine learning. The proposed approach preprocesses ECG images by converting them to grayscale, resizing, and segmenting them into 12 sections corresponding to standard ECG leads. Grid lines are removed, and the essential signals are extracted using contour techniques, transforming images into one-dimensional signals, which are then scaled and stored in a structured dataset. A classification model is developed using an ensemble machine learning approach, enhancing accuracy through a stacked combination of multiple algorithms. Despite the challenge of a limited dataset, the system provides instant results, making it particularly useful in remote areas with scarce medical resources. The architecture involves a web-based application where users upload ECG images, which are then processed through a pre-trained ML model to deliver diagnostic predictions. This system is not designed to replace traditional medical evaluations but rather to accelerate the diagnostic process, providing an accessible and efficient solution for early CVD detection

Cardiovascular Disease Prediction, ECG Image Processing, Machine Learning, Ensemble Classification, Digital Healthcare, Remote Diagnosis, Automated ECG Analysis

"Combining Heart Imaging with Advanced AI for Better Heart Disease Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.c711-c716, May-2025, Available :http://www.jetir.org/papers/JETIR2505294.pdf

"Combining Heart Imaging with Advanced AI for Better Heart Disease Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppc711-c716, May-2025, Available at : http://www.jetir.org/papers/JETIR2505294.pdf