Cardiovascular disease (CVD) is a life-threatening medical condition caused by high blood sugar, high blood pressure, alcohol, smoking, and congenital abnormalities. There are several ways to diagnose CVD, such as stethoscopes, electrocardiography (ECG), phonocardiography (PCG), and computed tomography (CT). Some of these devices are inaccurate and require medical expertise. In stethoscopes, the doctor’s skill determines heart sound interpretation and external disturbances can affect their effectiveness. Additionally, ECG and PCG cannot provide complete heart health information. CT scans, however, emit radiation that can harm patients. This study examines various CVD early detection and prediction methods to find the best one for patients and doctors. Previous research has identified methods, including artificial intelligence (AI) algorithms and wireless sensors. The study also seeks to identify the best model and examine CVD detection challenges. Comparing previous research, the modified scalp swarm optimization method and adaptive fuzzy neurological system (MSSO-ANFIS) had the highest rating accuracy, 97.45%. Researchers’ CVD detection difficulties were also highlighted in the paper. In conclusion, the use of echocardiography devices provides comprehensive and accurate information about the heart, including details such as the shape of the heart, the thickness of the heart muscle, and heart sounds.
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