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Title: :  Heat diseases prediction using machine learning
PaperId: :  26608
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 3 2025
DUI:    16.0415/IJARIIE-26608
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Prof. Meghashree M BVidya vikas institute of engineering and technology, Mysuru, Karnataka, India
Arjun P RVidya vikas institute of engineering and technology, Mysuru, Karnataka, India
Girisha R Vidya vikas institute of engineering and technology, Mysuru, Karnataka, India
Lakshman R Vidya vikas institute of engineering and technology, Mysuru, Karnataka, India
Tajuddin Vidya vikas institute of engineering and technology, Mysuru, Karnataka, India

Abstract

Computer Engineering
Heart Disease Prediction, Machine Learning, XGBoost, Healthcare Analytics, Early Diagnosis, Cardiovascular Risk
Heart disease is a critical global health issue and remains one of the leading causes of mortality, particularly among middle-aged and elderly populations. Traditional diagnostic methods such as angiography and stress testing, while effective, are often invasive, expensive, and not always accessible, especially in under-resourced regions. To address these limitations, this study explores the use of machine learning (ML) techniques to develop a predictive model that can assess the likelihood of heart disease based on clinical and lifestyle-related patient data. This research utilizes a publicly available dataset containing health-related records of over 400,000 individuals from the United States. The study involves the implementation and evaluation of six widely-used machine learning algorithms: XGBoost, Bagging Classifier, Random Forest, Decision Tree, K-Nearest Neighbors (KNN), and Naïve Bayes. Each model is trained and tested using standard performance evaluation metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, to determine their effectiveness in predicting heart disease. Among all the models evaluated, the XGBoost classifier demonstrated the highest predictive performance, achieving an accuracy of 91.30%. The superior results are attributed to XGBoost’s ability to handle complex feature interactions and its robustness against overfitting. This study emphasizes the potential of ML-driven approaches in building scalable, accurate, and cost-effective diagnostic tools that can assist healthcare providers in early detection and personalized risk assessment. By integrating such models into healthcare systems, this research aims to support timely clinical decision-making and ultimately contribute to reducing the global burden of heart disease.

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IJARIIE Prof. Meghashree M B, Arjun P R, Girisha R , Lakshman R , and Tajuddin . "Heat diseases prediction using machine learning" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 3 2025 Page 1357-1360
MLA Prof. Meghashree M B, Arjun P R, Girisha R , Lakshman R , and Tajuddin . "Heat diseases prediction using machine learning." International Journal Of Advance Research And Innovative Ideas In Education 11.3(2025) : 1357-1360.
APA Prof. Meghashree M B, Arjun P R, Girisha R , Lakshman R , & Tajuddin . (2025). Heat diseases prediction using machine learning. International Journal Of Advance Research And Innovative Ideas In Education, 11(3), 1357-1360.
Chicago Prof. Meghashree M B, Arjun P R, Girisha R , Lakshman R , and Tajuddin . "Heat diseases prediction using machine learning." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 3 (2025) : 1357-1360.
Oxford Prof. Meghashree M B, Arjun P R, Girisha R , Lakshman R , and Tajuddin . 'Heat diseases prediction using machine learning', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, 2025, p. 1357-1360. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Heat_diseases_prediction_using_machine_learning_ijariie26608.pdf (Accessed : ).
Harvard Prof. Meghashree M B, Arjun P R, Girisha R , Lakshman R , and Tajuddin . (2025) 'Heat diseases prediction using machine learning', International Journal Of Advance Research And Innovative Ideas In Education, 11(3), pp. 1357-1360IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Heat_diseases_prediction_using_machine_learning_ijariie26608.pdf (Accessed : )
IEEE Prof. Meghashree M B, Arjun P R, Girisha R , Lakshman R , and Tajuddin , "Heat diseases prediction using machine learning," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 3, pp. 1357-1360, May-Jun 2025. [Online]. Available: http://ijariie.com/AdminUploadPdf/Heat_diseases_prediction_using_machine_learning_ijariie26608.pdf [Accessed : ].
Turabian Prof. Meghashree M B, Arjun P R, Girisha R , Lakshman R , and Tajuddin . "Heat diseases prediction using machine learning." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 3 ().
Vancouver Prof. Meghashree M B, Arjun P R, Girisha R , Lakshman R , and Tajuddin . Heat diseases prediction using machine learning. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(3) : 1357-1360. Available from: http://ijariie.com/AdminUploadPdf/Heat_diseases_prediction_using_machine_learning_ijariie26608.pdf
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