Heart disease is the most common serious illness that affects human health and is also a cause of mortality, so it has become a major threat to human health. This is why automated diagnosis methods are needed to identify heart diseases accurately and effectively. The electrocardiogram is mostly usable by the physician and the medical field to detect heart diseases. The essential goal of this work is to identify different cardiac diseases by classifying ECG images into five classes using the traditional and ensemble machine learning techniques with the highest accuracy.
This study experiments with paper-based ECG images based on segmentation, feature extraction, and feature selection as well. In addition, it continued into two stages; in the first stage, after feature extraction and feature selection, traditional model as Decision Tree, KNN, SVM, LightGBM, MLP, Naive Bayes, and Logistic Regression and ensemble model were applied. In the second stage, after performing the segmentation, feature extraction, and feature selection; again, run the tradition and the ensemble model. Before training the model, several image pre-processing techniques have been applied to remove the image patterns and enrich the image quality.
Early identification and adequate treatment can assist to prevent lung and COVID disorders from becoming chronic, severe, and life-threatening, lowering the death rate by identifying at an early stage. Furthermore, X-ray images are commonly advised since the methods are less expensive, quicker, and expose patients to less radiation
Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone
