Diagnosis of Chronic Ischemic Heart Disease Using Machine Learning Techniques

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 9
• Main Authors: Shehzadi, Shumaila, Hassan, Muhammad Abul, Rizwan, Muhammad, Kryvinska, Natalia, Vincent, Karovič
• Format: Journal Article
• Language: English
• Published: New York Hindawi 14.06.2022; John Wiley & Sons, Inc
• Subjects: Accuracy; Analysis; Cardiovascular disease; Chronic diseases; Classification; Coronary artery disease; Coronary heart disease; Data mining; Data processing; Datasets; Diagnosis; Fuzzy logic; Genetic algorithms; Health aspects; Heart; Heart attacks; Heart diseases; Illnesses; Internet of Things; Irritation; Ischemia; Learning algorithms; Machine learning; Medical research; Medicine, Experimental; Methods; Mortality; Myocardial ischemia; Neural networks; Pakistan; Patient satisfaction; Prognosis; Risk factors; Pakistan
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/3823350

Ischemic heart disease (IHD) causes discomfort or irritation in the chest. According to the World Health Organization, coronary heart disease is the major cause of mortality in Pakistan. Accurate model with the highest precision is necessary to avoid fatalities. Previously several models are tried with different attributes to enhance the detection accuracy but failed to do so. In this research study, an artificial approach to categorize the current stage of heart disease is carried out. Our model predicts a precise diagnosis of chronic diseases. The system is trained using a training dataset and then tested using a test dataset. Machine learning methods such as LR, NB, and RF are applied to forecast the development of a disease. Experimental outcomes of this research study have proven that our strategy has excelled other procedures with maximum accuracy of 99 percent for RF, 97 percent for NB, and 98 percent for LR. With such high accuracy, the number of deaths per year of ischemic heart disease will be slightly decreased.