Abstract :

Heart Disease Remains One Of The Leading Causes Of Mortality Worldwide, Creating A Critical Need For Accurate And Efficient Prediction Systems That Assist Medical Professionals In Early Diagnosis And Treatment Planning. This Study Proposes HDPM (Heart Disease Prediction Model), An Effective Predictive Framework Designed For Integration Within A Clinical Decision Support System (CDSS). The Model Utilizes Machine Learning Techniques To Analyze Patient Health Records, Including Clinical Parameters Such As Age, Blood Pressure, Cholesterol Levels, Heart Rate, And Other Relevant Medical Attributes. By Employing Data Preprocessing, Feature Selection, And Supervised Learning Algorithms, The Proposed System Aims To Improve Prediction Accuracy While Reducing Diagnostic Complexity. The HDPM Framework Is Capable Of Identifying Patterns And Risk Factors Associated With Heart Disease, Enabling Healthcare Providers To Make Informed Decisions At Earlier Stages Of Patient Evaluation. Experimental Evaluation Demonstrates That The Proposed Model Achieves Higher Accuracy, Reliability, And Efficiency Compared To Traditional Diagnostic Approaches. The Integration Of HDPM Into A Clinical Decision Support Environment Enhances The Ability Of Healthcare Professionals To Detect Heart Disease Risks Promptly, Thereby Supporting Preventive Care And Improving Patient Outcomes

Published:

09-10-2022

Issue:

Vol. 22 No. 10 (2022)

Page Nos:

24 - 32

Section:

Articles

License:

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

How to Cite