Detection of Cardiovascular Disease Using AI

Detecting illnesses in their early stages can help prevent serious complications and improve treatment outcomes. Early diagnosis is critical because it reduces the risk of severe consequences, as prevention is better than cure. A high death rate often occurs when diseases are not detected early. Expert systems can bridge this gap by diagnosing diseases automatically in their initial phases. These systems use fuzzy, rule-based engines to analyze patient data and apply forward-chaining techniques for diagnosis. In this study, data such as age, gender, blood sugar levels, blood pressure, and ECG results were collected from various sources, including hospitals, to evaluate patients' health conditions. For example, the expert system might analyze the risk of heart disease and classify it as "low," "high," or "critical." Based on the system's diagnosis, a doctor determines the appropriate treatment and prescribes medication according to the risk level. The study shows that this approach significantly improves the accuracy of detecting heart disease risk levels and supports doctors in providing effective treatment to patients. Keywords: expert system; fuzzy logic; ECG; heart disease; blood sugar; cholesterol.