Abstract: Heart disease describes a range of conditions that affect your heart. Diseases under the heart disease umbrella include blood vessel diseases, such as coronary artery disease, heart rhythm problems (arrhythmias), and heart defects you're born with (congenital heart defects), among others. According to World Health Organization (WHO), cardiovascular disease (CVD) is one of the most lethal diseases that leads to the most number of deaths worldwide. Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease using the device's vital parameters like body temperature, heart rate, and blood pressure. This project proposes a Random Forest technique as the backbone of computer-aided diagnostic tools for more accurately forecasting heart disease risk levels and sending alert messages to the doctor and the guardian with the location details of the patient. Random Forest modeling is a promising classification approach for predicting medication adherence in CVD patients. This predictive model helps stratify the patients so that evidence-based decisions can be made and patients managed appropriately. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset.
Keywords: CVD (cardiovascular disease), Random Forest algorithm, Machine learning, WHO (world health organization).
|
DOI:
10.17148/IJARCCE.2023.12507
