Published Paper Details:

The machine learning system called CardioMyx analyzes multiple medical parameters through its designed algorithm to detect heart disease risk in individuals. This system applies the Random Forest Classifier ensemble learning technique as a robust method to evaluate patient information which generates high-risk or low-risk categories. The model analyzes age, sex, cholesterol levels, blood pressure, and blood sugar together with lifestyle factors to predict precise risk ratings. Medical personnel gain essential diagnostic insights from advanced analytics built into CardioMyx which leads to fast medical interventions. The technical platform supports preventive cardiology decisions by letting doctors provide individualized medical treatments or lifestyle recommendations based on calculated risk outcomes. As extra educational data enters the model it becomes better at producing accurate risk results and maintains such precision across multiple patient populations. CardioMyx shows promise to change heart disease prognosis by cutting down human mistakes while supporting physicians to detect diseases early. Through its functions the system assists healthcare providers while allowing patients to understand their cardiac state which promotes active cardiovascular risk management.