Early Diagnosis of Heart Attack Using Predictive Analytics

Early Diagnosis of Heart Attack Using Predictive Analytics

Mangala H S1, Amogha Siddarth2, Amruth M3, Govind Venkatesh4, Hemanth M5

1Mangala H S, Computer Science & Engineering, Dayananda Sagar Academy of Technology & Management

2Amogha Siddarth, Computer Science & Engineering, Dayananda Sagar Academy of Technology & Management

3Amruth M, Computer Science & Engineering, Dayananda Sagar Academy of Technology & Management

4Govind Venkatesh , Computer Science & Engineering, Dayananda Sagar Academy of Technology & Management

5Hemanth M, Computer Science & Engineering, Dayananda Sagar Academy of Technology & Management

Abstract - 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.

Key Words: Age, Blood, pressure, Cholesterol, ECG, Heart rate, Data preprocessing, Feature selection, Model training, Performance evaluation, Logistic Regression, Random Forest