AI based Supervised Learning Approach for Heart Disease Prediction and Prevention

Abstract

Heart disease remains a leading cause of mortality worldwide, making early detection and prevention critical. This project, \"AI based Supervised Learning Approach for Heart Disease Prediction and Prevention,\" employs Bagging Classifier and Deep Learning techniques to predict heart disease risk, classify its stages, and recommend preventive measures. The Bagging Classifier enhances accuracy by reducing variance through ensemble learning, while deep learning models analyze patient data—including age, sex, blood pressure, and heart rate—to determine disease severity. The system is trained on real-time and historical medical records to improve reliability. Additionally, it provides personalized preventive recommendations based on a patient\'s risk level and disease stage, guiding users on lifestyle modifications, dietary changes, and medical interventions. The proposed system offers a fast, accurate, and cost-effective solution to reduce heart disease-related mortality and improve patient outcomes.

Introduction

Heart disease is a leading cause of death worldwide, accounting for about 31% of global fatalities annually. Early diagnosis and intervention are crucial but traditional diagnostic methods rely heavily on manual analysis, which can be slow and error-prone. To improve this, the project proposes an AI-based system combining supervised learning (Bagging Classifier) and deep learning (Lenet architecture) to predict heart disease, assess severity, and recommend personalized prevention.

The system uses common physiological inputs like age, sex, blood pressure, and heart rate, making it practical for widespread use. The Bagging Classifier improves prediction accuracy by aggregating multiple models, while deep learning refines disease staging. The AI-driven tool aims to support real-time, accurate clinical decisions and empower both patients and healthcare providers.

Literature reviews show various machine learning techniques (Naive Bayes, SVM, KNN) have been used in heart disease prediction, with ensemble methods like Bagging enhancing accuracy. However, existing models often lack severity classification and personalized recommendations.

The proposed system involves data preprocessing (cleaning, normalization) of datasets (e.g., from Kaggle), and uses Python-based tools (Scikit-learn, TensorFlow, Pandas) for modeling. Initial results indicate a high prediction accuracy (~98.76%), suggesting significant potential for real-time heart disease prediction and prevention, improving healthcare outcomes globally.

Conclusion

In this research, we successfully developed an AI driven heart disease prediction system that achieved an accuracy of approximately 96% using the Bagging Classifier. The ensemble learning approach significantly improved prediction reliability by reducing overfitting and enhancing generalization. Additionally, deep learning techniques were employed to classify heart disease into different stages of severity, enabling a more precise understanding of disease progression. The system integrates real-time health monitoring for continuous tracking of heart rate and blood pressure, ensuring early detection and timely intervention. The deployment of the model as a Django-based web application makes it accessible to both healthcare professionals and individuals, promoting ease of use and scalability. Furthermore, data visualization tools enhance the interpretability of predictions, supporting better clinical decision-making. Our results demonstrate that machine learning and deep learning can significantly contribute to the early diagnosis and prevention of heart disease. The high accuracy achieved with the Bagging Classifier confirms the effectiveness of the proposed approach. Future work could focus on integrating additional health parameters and realtime sensor-based monitoring to further enhance the model’s performance and usability. This research highlights the potential of AI in revolutionizing cardiovascular healthcare, providing a proactive solution for disease prevention and management

References

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Copyright

Copyright © 2025 Dinesh D, Dinesh K, Sivaperumal V, Mr. K. Balaji. 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.