A Hybrid Machine Learning Approach for Cardiovascular Disease Prediction

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Year : 2025 | Volume :12 | Issue : 01 | Page : –
By
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K. Purushotam Naidu,

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V Lakshmana Rao,

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Esha Thaniya Malla,

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Indu Kola,

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Renuka Sai Reddi,

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Bharathi Kolluru,

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Raj Tanuja Pentapati,

  1. Assistant Professor, Department of Computer Science and Engineering (AI&ML), GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  2. Assistant Professor, Department of Computer Science and Engineering, GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  3. Student, Department of Computer Science and Engineering, (AI&ML), GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  4. Student, Department of Computer Science and Engineering, (AI&ML), GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  5. Student, Department of Computer Science and Engineering, (AI&ML), GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  6. Student, Department of Computer Science and Engineering, (AI&ML), GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India
  7. Student, Department of Computer Science and Engineering, (AI&ML), GVP College of Engineering for Women, Visakhapatnam, Andhra Pradesh, India

Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_130122’);});Edit Abstract & Keyword

Heart disease ranks among the top causes of death globally. Accurately predicting cardiovascular conditions has become a key challenge in the realm of clinical data analysis. It has been shown that machine learning is an effective means of assisting with predicting and decision-making based on the large volume of data produced by the medical industry. In this study, we describe a unique approach that increases the prediction accuracy of heart-related conditions by using machine learning approaches to identify important attributes. The prediction model is displayed using a variety of feature combinations and widely used categorization techniques. With an accuracy level of 85.7%, we attain an enhanced performance level with the Hybrid Random Forest with Logistic Model (HRFLM) prediction model for heart disease. This adds to the continuing conversation in healthcare analytics and lays a solid basis for clinical decision support’s use of data-driven predictive models. In this case, machine learning and is an effective way to solve the challenges associated with cardiovascular disease prediction. The acquired results highlight the potential of using cutting-edge analytics and novel predictive modelling tools to enhance patient outcomes and well-being.

Keywords: Clinical Data Analysis, Hybrid Model, Risk Assessment, Heart Disease Prediction, Cardiovascular Disease, Healthcare Industry

[This article belongs to Journal of Artificial Intelligence Research & Advances (joaira)]

How to cite this article:
K. Purushotam Naidu, V Lakshmana Rao, Esha Thaniya Malla, Indu Kola, Renuka Sai Reddi, Bharathi Kolluru, Raj Tanuja Pentapati. A Hybrid Machine Learning Approach for Cardiovascular Disease Prediction. Journal of Artificial Intelligence Research & Advances. 2024; 12(01):-.
How to cite this URL:
K. Purushotam Naidu, V Lakshmana Rao, Esha Thaniya Malla, Indu Kola, Renuka Sai Reddi, Bharathi Kolluru, Raj Tanuja Pentapati. A Hybrid Machine Learning Approach for Cardiovascular Disease Prediction. Journal of Artificial Intelligence Research & Advances. 2024; 12(01):-. Available from: https://journals.stmjournals.com/joaira/article=2024/view=0

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Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 05/10/2024
Accepted 05/12/2024
Published 30/12/2024
175