Calories Burnt Prediction using Machine Learning

Abstract

Using machine learning (ML) techniques, this study examines the prediction of calories burnt based on various physiological and activity-related parameters. The study employs classification and regression algorithms to forecast calorie expenditure by analysing datasets containing attributes such as age, weight, heart rate, and exercise duration. The results highlight key influencing factors and contribute to a deeper understanding of how machine learning can enhance predictive modelling in health and fitness research.

Introduction

I. Overview

This research explores how machine learning (ML) can improve the prediction of calories burned during physical activity, overcoming limitations of traditional methods (e.g., metabolic equations) that fail to account for individual variability in metabolism, exercise intensity, and other physiological factors. The goal is to build accurate, personalized, and adaptive models using real-world data.

II. Literature Review

• Traditional calorie estimation methods (e.g., Harris-Benedict, METs) lack precision.

• Ensemble models (e.g., Random Forest, XGBoost) have shown superior accuracy over linear models.

• Feature selection techniques like RFE and univariate selection improve model efficiency by focusing on impactful variables such as heart rate and duration.

• Deep learning can model complex relationships but is resource-intensive.

• Real-time prediction using wearable data and robust preprocessing are critical for reliable outputs.

III. Related Work

• ML is widely used in health applications, with models like Decision Trees, Random Forests, and Neural Networks outperforming traditional approaches.

• Challenges include inconsistent sensor data, individual metabolic variation, and data quality issues, all of which affect prediction accuracy.

IV. Methodology

A. Dataset

• Sourced from Kaggle, the dataset includes 15,000+ records with attributes like gender, age, height, weight, heart rate, exercise duration, and body temperature.

B. Data Preprocessing

• Cleaning steps included handling missing values and outliers.

• Dataset split: 80% training / 20% testing.

C. Models Evaluated

• Support Vector Machine (SVM)

• Random Forest

• Linear Regression

• XGBoost Regression

Performance measured using:

• Mean Absolute Error (MAE)

• Root Mean Squared Error (RMSE)

• R² Score

D. Feature Selection

• Techniques like Univariate Selection and RFE were used to improve model accuracy.

• Correlation analysis identified the most relevant features.

E. Key Features Identified
Top contributors to accurate calorie prediction:

• Heart Rate

• Exercise Duration

• Body Temperature

F. Visualization

• Data patterns and distributions were visualized using Seaborn and Matplotlib to support feature analysis.

Conclusion

This study explored the application of machine learning techniques for predicting calories burnt during physical activity, aiming to enhance the accuracy of fitness tracking and health monitoring systems. By leveraging various machine learning models, including Support Vector Machine (SVM), Random Forest, Linear Regression, and Boost Regression, we assessed their performance in predicting calorie expenditure based on key physiological and activity-related features. Our results indicate that Boost outperformed other models, demonstrating superior accuracy in calorie prediction. Key contributing factors such as heart rate, exercise duration, body temperature, weight, and height played a crucial role in determining caloric burn, emphasizing the importance of feature selection and pre-processing in improving model efficiency. Correlation analysis and visualization techniques further helped in understanding relationships between variables, leading to improved model interpretability. Despite these promising results, certain limitations were observed. The dataset size and diversity could influence model generalization, and external factors such as diet, hydration levels, and metabolic variations were not included in the current model. Future research should focus on integrating real-time wearable sensor data, incorporating deep learning techniques, and expanding datasets to improve prediction reliability.

References

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Copyright

Copyright © 2025 Abu Rayyan, Akshat Kumar, Yadav Anuj, Arman Ahmad, Dr. Abdul Alim, Dr. Shweta Rai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.