Early Diagnosis of Heart Attack Using Predictive Analytics

– Heart disease, particularly heart attacks, remains one of the leading causes of mortality worldwide. Timely and accurate diagnosis is critical to improving survival rates and reducing long-term complications. This project explores the application of predictive analytics for the early diagnosis of heart attacks, utilizing historical patient data and machine learning techniques to identify patterns and risk factors. By analyzing parameters such as age, blood pressure, cholesterol levels, ECG results, heart rate, and other clinical indicators, predictive models can effectively estimate the likelihood of a heart attack before it occurs. The study involves data preprocessing, feature selection, model training, and performance evaluation using algorithms such as Logistic Regression, Random Forest, and Support Vector Machines. The aim is to assist healthcare professionals in making informed decisions, enabling preventive care and timely medical intervention. This approach not only enhances diagnostic accuracy but also paves the way for more personalized and data driven healthcare solutions.