Early Detection and Diagnosis of Cardiac Disorders using Machine Learning Techniques

Heart disease (HD) is a wide spread health concern that impacts a significant number of people worldwide. Common symptoms of heart disease (HD) include swollen feet, weakness in the body, and shortness of breath. The recent advancements in ML has made significant role in transforming different diagnostic approaches Due to a variety of factors, current diagnostic methods are not very effective in early detection. To offer a practical approach for identifying cardiac disease, scientists are trying to increase precision and execution time. Heart disease diagnosis and treatment can be challenging in areas with limited access to modern medical professionals and equipment. With the right diagnosis and care, many lives can be saved. According to estimations from the European Society of Cardiology, HD affects 26 million individuals. Nowadays, a higher proportion of individuals globally have heart failure, a complex clinical condition.ECG is the primary tool used by hospitals and cardiac centres to evaluate and diagnose heart failure in its early stages. One may think of the ECG as a standard tool. Early identification of heart disease is a major priority for medical services. This paper mainly highlights improving cardiac care through early diagnostic contributing to better patient outcomes and reduced health risks.