Deep Learning for Heart Disease Prediction with Improved Normalization Techniques

Abstract

Heart disease is a major global health concern, responsible for millions of deaths annually. Early diagnosis plays a crucial role in reducing mortality rates and improving patient outcomes. However, traditional diagnostic approaches, such as ECG analysis, stress tests, and cholesterol measurements, rely heavily on manual interpretation by medical professionals. These methods are not only time-consuming but also subject to human error and variability in expertise. With the rapid advancements in artificial intelligence (AI), deep learning has emerged as a powerful tool in the field of medical diagnostics. These models can process vast amounts of cardiovascular data, identify complex patterns, and provide more accurate predictions compared to conventional methods. One of the key factors influencing the performance of deep learning models is data preprocessing, particularly normalization. Normalization ensures that different features within a dataset are appropriately scaled, preventing issues such as numerical instability and poor model convergence. In this study, we explore and compare various normalization techniques, including Min-Max Scaling, Standardization, Batch Normalization, and Layer Normalization, to improve the efficiency and accuracy of deep learning models for heart disease prediction. To evaluate the impact of these normalization methods, we trained deep learning models using a comprehensive cardiovascular dataset. Experimental results demonstrated that advanced normalization techniques significantly enhance model performance. Batch Normalization improves training speed by reducing internal covariate shifts, whereas Layer Normalization ensures consistent performance across different architectures. These improvements contribute to better generalization, leading to more reliable heart disease predictions. By optimizing preprocessing techniques, this research aims to advance AI-driven healthcare solutions, enabling faster and more accurate diagnoses. Future work could focus on integrating these models into real-time monitoring systems to further enhance early detection and personalized treatment strategies. This study serves as a step towards the broader adoption of AI in clinical decision-making, ultimately improving patient care and reducing the burden of heart disease on healthcare systems.

Introduction

Problem Statement:

Heart disease is a leading global health concern, causing millions of deaths annually. Key risk factors include poor lifestyle habits, genetics, and stress. Early detection is crucial but traditional diagnostic methods are slow and often rely heavily on medical professionals.

Proposed Solution:

The project aims to enhance heart disease prediction using deep learning supported by advanced normalization techniques, particularly:

• Batch Normalization (BN)

• Layer Normalization

These techniques improve training efficiency, stability, and model performance by addressing issues like internal covariate shift in neural networks.

Methodology:

1. Data Preprocessing:

   • Load and clean cardiovascular datasets

   • Encode categorical variables

   • Balance classes using SMOTE

2. Model Training:

   • Train ANN (Artificial Neural Network) and XGBoost on selected features

   • Apply Batch Normalization between layers

   • Use ensemble method with weighted average for final predictions

3. Deployment:

   • Models and preprocessing tools saved

   • System deployed using Flask

Results:

• The proposed system (ANN with Batch Normalization) achieved 92% accuracy, outperforming existing models:

  • SVM: 81.97%

  • KNN: 67.2%

  • Decision Tree: 81.97%

  • ANN (without BN): 85.24%

Related Work Insights:

• Prior studies explored hybrid models and feature selection techniques (e.g., DenseNet with optimization algorithms, ensemble ML-DL approaches).

• While they improved accuracy, issues like high computational cost and overfitting persisted.

• Most lacked efficient preprocessing, real-time deployment readiness, or scalability.

Key Findings:

• Batch Normalization enhances training speed, generalization, and predictive accuracy across deep learning architectures.

• Data preprocessing and normalization play a critical role in improving model performance in medical AI applications.

• This research offers a robust AI-driven tool for early heart disease detection, supporting faster and more accurate medical decisions.

Conclusion

Wedeveloped and evaluated a machine learning model for predicting the presence of heart disease using clinical and diagnostic features. We applied data preprocessing techniques, including feature selection, normalization, and class balancing using SMOTE, to improve model performance. Several models were explored, including Artificial Neural Networks (ANN), XGBoost [15], and ensemble approaches.

References

[1] https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) [2] https://world-heart-federation.org/what-is-cvd/ [3] https://pmc.ncbi.nlm.nih.gov/articles/PMC7485162/ [4] https://www.researchgate.net/publication/378803559_Hybrid_Optimization_based_Feature_Selection_with_DenseNet_Model_for_Heart_Disease_Prediction [5] https://www.researchgate.net/publication/336462310_Deep_Learning_to_Improve_Heart_Disease_Risk_Prediction [6] https://www.researchgate.net/publication/357566449_Heart_disease_prediction_model_with_k-nearest_neighbor_algorithm [7] https://www.researchgate.net/publication/379561252_Heart_Disease_Prediction_Using_Machine_Learning [8] https://www.researchgate.net/publication/341831889_Heart_Diseases_Prediction_using_Deep_Learning_Neural_Network_Model [9] https://www.analyticsvidhya.com/blog/2021/03/introduction-to-batch-normalization/ [10] ]https://medium.com/@piyushkashyap045/understanding-batch-normalization-in-deep-learning-a-beginners-guide-40917c5bebc8 [11] https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43442.pdf [12] https://www.pinecone.io/learn/batch-layer-normalization/ [13] https://iopscience.iop.org/article/10.1088/1757-899X/981/2/022006 [14] https://www.baeldung.com/cs/batch-normalization-cnn [15] https://www.ibm.com/think/topics/xgboost

Copyright

Copyright © 2025 Ms. Shubhangi Mahule, Gali Lahari Reddy, Seelam Sai Varsha, Dakuri Sairam Reddy, Vennavelli Pradeep Reddy. 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.