Abstract :

Obesity Is A Major Global Health Concern That Significantly Affects Various Physiological Systems, Including The Autonomic Nervous System (ANS). The ANS Plays A Crucial Role In Regulating Heart Rate, Blood Pressure, And Other Vital Functions, And Its Imbalance Can Lead To Serious Cardiovascular Complications. Heart Rate Variability (HRV) Is A Non-invasive Measure Widely Used To Assess The Functioning Of The ANS. This Project Proposes A Machine Learning-based Approach To Study The Impact Of Obesity On The Autonomic Nervous System Using HRV Features. The System Utilizes HRV Datasets Collected From Individuals With Different Body Mass Index (BMI) Levels. Preprocessing Techniques Such As Noise Removal, Normalization, And Feature Extraction Are Applied To Obtain Meaningful HRV Parameters, Including Time-domain, Frequencydomain, And Non-linear Features. Machine Learning Algorithms Such As Support Vector Machines (SVM), Random Forest, And Logistic Regression Are Employed To Classify Subjects Based On Obesity Levels And Analyze ANS Activity. The Dataset Is Divided Into Training And Testing Sets To Evaluate Model Performance Using Metrics Such As Accuracy, Precision, Recall, And F1-score. Experimental Results Demonstrate That Machine Learning Models Can Effectively Identify Patterns In HRV Data And Distinguish Between Obese And Non-obese Individuals With High Accuracy. This Approach Provides Valuable Insights Into The Relationship Between Obesity And ANS Dysfunction, Enabling Early Detection Of Health Risks And Supporting Preventive Healthcare Strategies.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1872-1877

Section:

Articles

License:

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

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