Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods

IEEE BASE PAPER ABSTRACT:

Cardiovascular diseases (heart diseases) are the leading cause of death worldwide. The earlier they can be predicted and classified; the more lives can be saved. Electro-cardiogram (ECG) is a common, inexpensive, and noninvasive tool for measuring the electrical activity of the heart and is used to detect cardiovascular disease. In this work, the power of deep learning techniques was used to predict the four major cardiac abnormalities: abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal person classes using the public ECG images dataset of cardiac patients. First, the transfer learning approach was investigated using the low-scale pre-trained deep neural networks SqueezeNet and AlexNet. Second, a new Convolutional Neural Network (CNN) architecture was proposed for cardiac abnormality prediction. Third, the afore-mentioned pretrained models and our proposed CNN model were used as feature extraction tools for traditional machine learning algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Decision Tree (DT), Random Forest (RF), and Naïve Bayes (NB). According to the experimental results, the performance metrics of the proposed CNN model outperform the exiting works; it achieves 98.23% accuracy, 98.22% recall, 98.31% precision, and 98.21% F1 score. Moreover, when the proposed CNN model is used for feature extraction, it achieves the best score of 99.79% using the NB algorithm.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

MobileNet Architecture.

OUR PROPOSED ABSTRACT:

Over the past few decades, cardiovascular illnesses have become the leading cause of death worldwide in both industrialised and developing nations. The mortality rate can be decreased through early identification of heart disorders and ongoing clinical monitoring by professionals. Cardiovascular disease is extremely lethal by nature and kills a disproportionately large number of people worldwide. An effective early detection strategy is crucial to preventing cardiovascular disease deaths.

An electrocardiogram (ECG) is a vital tool for understanding a variety of human heart problems. This subject has been the subject of numerous investigations to identify heart anomalies for prevention. In order to forecast cardiovascular disorders, this research intends to construct an algorithmic model to analyse ECG tracings. This effort directly affects saving lives and enhancing healthcare at a lower cost. Saving lives and enhancing medical treatment are two immediate effects of this work as health care and health insurance expenses rise globally.

In this study, the public ECG picture dataset of cardiac patients was used to harness the potential of deep learning techniques to predict the four main cardiac abnormalities: abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal person classes.

We used the MobileNet Architecture to build the system for this project, and we were successful in achieving training accuracy of 97.34% and validation accuracy of 91.00%. As a result, the proposed MobileNet Architecture model classifies cardiovascular diseases with impressive accuracy and can also be utilised to extract features for conventional machine learning classifiers. Bypassing the manual method that produces unreliable and time-consuming findings, the suggested MobileNet Architecture model can be utilised as a tool to assist physicians in the medical profession in detecting heart disorders using ECG images.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating system : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask
Frontend : HTML, CSS, JavaScript.

REFERENCE:

Mohammed B. Abubaker, “Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods”, IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE, VOL. 4, NO. 2, APRIL 2023.

Python IEEE Projects

Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods