Hybrid Learning Model for Cardiovascular Risk Assessment

Abstract

Cardiovascular diseases constitute a significant global health concern, demanding accurate predictive tools for timely intervention. This study addresses this imperative by employing a hybrid machine learning approach, integrating Random Forest and Linear Regression techniques for forecasting heart diseases. The complexity of cardiovascular dynamics requires a nuanced methodology, and this research endeavors to enhance forecasting precision without predefining the model structure. Leveraging a diverse patient dataset encompassing age, cholesterol levels, blood pressure, and lifestyle factors, the hybrid framework is trained on historical data, leveraging Random Forest for intricate relationships and Linear Regression for interpretability. Through meticulous feature importance analysis, the study unveils key factors influencing heart disease prognosis, offering valuable insights for personalized healthcare initiatives. This hybrid methodology, without reliance on a predetermined model, holds promise for improving early prediction strategies and contributing to more effective interventions in cardiovascular health.

Introduction

Cardiovascular diseases (CVDs), including heart disease, are a major global health issue, contributing significantly to mortality and healthcare costs. Early prediction and diagnosis are crucial, and machine learning (ML) offers promising solutions to enhance accuracy and efficiency in detecting heart disease using patient data.

Machine Learning Techniques:

1. Linear Regression:

   • A statistical method that models the linear relationship between patient features (e.g., age, cholesterol, blood pressure) and heart disease risk.

   • Offers simplicity, transparency, and interpretability.

2. Random Forest:

   • An ensemble learning algorithm made up of multiple decision trees.

   • Captures complex, non-linear relationships between variables.

   • Robust and effective for healthcare data with intricate feature interactions.

Proposed System: Cardiovascular Disease Prediction System (CDPS)

• Aims to build a highly accurate ML-based prediction system using a variety of algorithms:

  • REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, JRIP

• Models are evaluated using accuracy, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and prediction time.

Key Findings:

• Random Tree Model showed outstanding performance:

  • Accuracy: 100%

  • MAE: 0.0011

  • RMSE: 0.0231

  • Prediction Time: 0.01 seconds

• The combination of Random Forest and Linear Models also achieved high predictive accuracy (92%), highlighting the power of hybrid approaches.

Impact and Future Outlook:

• ML enables data-driven, precise, and rapid predictions of heart disease.

• Improves early diagnosis and optimizes treatment strategies.

• Signals a paradigm shift in healthcare, moving toward automated, intelligent diagnostics.

• Future work includes refining model accuracy, integrating real-time prediction, and expanding the use of health data for broader applications.

Conclusion

The Prediction of heart disease presents a critical challenge given its complex nature and the involvement of multiple risk factors with nonlinear relationships. While algorithms like random forest can address complexity, there is a risk of overfitting, and linear regression might oversimplify the issue. The solution suggests leveraging the strengths of different algorithms through a combination approach. This strategy aims to enhance predictive accuracy, offering a more nuanced understanding of the intricate relationships in heart diseases while mitigating the limitations of individual models. Ultimately, this integrative approach holds promise for improving early detection and treatment of heart conditions, marking a step forward in addressing the multifaceted challenges posed by cardiovascular diseases.

References

[1] Rajkumar Gangappa Nadakunamani, Reyana, Sandeep Kautish,A.S. Vibith,Yogita Gupta,Sayed F.Abdelwahab and Wagdy Mohamed “Clinical Data Analysis for Prediction of Cardiovascular Disease Using Machine Learning Techniques”.2023 [2] Kompella Sri Charan and Kolluru S S N S Mahendra Nath,”Heart Disease Prediction Using Random Forest Algorithm”.vol 09, 2022 [3] Karna Vishnu Vardhana Reddy, Irraivan Elamvazuthi, Azrina Abd Aziz, Sivajothi Paramasivam, Hui Na Chua and S. Pranavanand, “Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators”,MDPI, 2021. [4] M. Snehith Raja, M. Anurag, Ch. Prachetan Reddy, Nageswara Rao Sirisala. \"Machine Learning Based Heart Disease Prediction System\". In proceeding of International Conference on Computer Communication and Informatics, IEEE, pp. 12-28, 2021. [5] Pronab Ghosh, Sami Azam, Mirjam Jonkman, Member, IEEE, Asif Karim, F.M.Javed Mehedi Shamrat, Eva Ignatious, Shahana Shultana, Abhijith Reddy Beeravolu and Friso De Boer, \"Efficient prediction of cardiovascular disease using machine learning algorithms with Relief and LASSO feature selection techniques\", vol.9, pp. 19304-19326, IEEE, 2021 [6] E. I. Elsedimy1 • Sara M. M. AboHashish1 • Fahad Algarni ,“New cardiovascular disease prediction approach using support vector machine and quantum behaved particle swarmoptimization”,Springer,2023 [7] Nadikatla Chandrashekar and Samineni Peddakrishna, “Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization”, MDPI,2023. [8] Joung ouk(Ryan) Kim, Yong-Suk Jeong, Jin Ho Kim, Jong Weon Lee, Dougho Park and Hyoung-Seop Kim, “Machine Learning-Based Cardiovascular Disease Prediction Model: A Cohort Study on the Korean National Health Insurance Service Health Screening Database”,MDPI,2021. [9] Osman Taylan, Abdulaziz S. Alkabaa, Hanan S. Alqabbaa, Esra Pamukcu and Victor Leiva, “Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods”,MDPI,2023. [10] Vijeta Sharma, Shrinkhala yadav,Manjari Gupta \"Heart disease prediction using Machine Learning Techniques\", International Conference on Advances in Computing, Communication Control and Networking (ICACCCN),IEEE, 9362842,2021.

Copyright

Copyright © 2025 Sonika Harshitha, Dr. Sankhya N Nayak . 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.