Abstract :

Heart Disease Remains One Of The Leading Causes Of Mortality Worldwide, Making Early Detection And Accurate Diagnosis Crucial For Reducing Death Rates. Traditional Diagnostic Methods Often Rely On Clinical Expertise And Medical Tests, Which Can Be Time-consuming, Expensive, And Sometimes Prone To Human Error. With The Advancement Of Machine Learning And Artificial Intelligence, Automated Systems Have Been Developed To Analyze Large Volumes Of Medical Data And Assist In Early Identification Of Heart Disease. These Systems Utilize Patient Health Parameters Such As Age, Blood Pressure, Cholesterol Levels, And ECG Signals To Predict The Likelihood Of Cardiovascular Conditions.Recent Studies Demonstrate That Machine Learning Models Such As Decision Trees, Support Vector Machines, Random Forest, And Neural Networks Significantly Improve Prediction Accuracy And Efficiency. These Models Can Detect Hidden Patterns In Medical Datasets And Provide Faster, More Reliable Predictions Compared To Traditional Methods. Furthermore, The Integration Of Explainable AI Enhances Transparency In Decision-making, Allowing Healthcare Professionals To Understand And Trust Model Outputs. As A Result, AI-based Heart Disease Identification Systems Are Becoming Essential Tools In Modern Healthcare For Early Diagnosis And Preventive Care

Published:

09-10-2022

Issue:

Vol. 22 No. 10 (2022)

Page Nos:

46 - 50

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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