Cardiocare Ai: Predictive Risk Assessment For Acute Myocardial Infarction Using Machine Learning

• Author(s):

  YV Akash | V Deepak | S Gurumoorthy | S Jeeva | R Vijay

• Keywords:

  AMI, XGBoost, AI, ML.

• Abstract:

  Acute Myocardial Infarction (AMI), Or Heart Attack Is A Severe Condition Caused By Reduced Blood Flow To The Heart. Early Detection Is Crucial To Lower Its Global Impact On Health. This Project Presents CardioCare AI, A Machine Learning Based Model For Predicting And Assessing AMI Risk. By Analyzing Data Like Cholesterol Blood Pressure, Blood Sugar, Smoking Habits, And Family History, It Identifies High Risk Individuals With Great Accuracy. Using Advanced Algorithms Like XGBoost Known For Handling Medical Data Effectively, The System Detects Patterns And Relationships Among Clinical Features. CardioCare AI Focuses On Non- Invasive Data To Ensure Its Accessibility For Widespread Use. It Provides Healthcare Professionals With Actionable Insights For Early Intervention, Enabling Preventive Care And Personalized Treatments. The Model Integrates Predictive Analytics Into Daily Medical Practices To Enhance Diagnostic Speed And Reliability, Addressing Limitations Of Traditional Methods. This Innovative Approach Aims To Improve Patient Outcomes Reduce Healthcare Challenges, And Support Global Cardiovascular Disease Prevention Efforts. By Offering A Scalable Solution, CardioCare AI Represented.

---

Other Details

• Paper id:

  IJSARTV11I5103515

• Published in:

  Volume: 11 Issue: 5 May 2025

• Publication Date:

  2025-05-09

Download Article