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Cardiovascular diseases (CVDs) have a high mortality rate globally, placing a significant burden on healthcare systems. Improvements in early diagnosis techniques are crucial to address the increasing prevalence of CVDs and their associated risks. CVDs are influenced by various factors such as high blood pressure, family history, stress, age, gender, cholesterol levels, and unhealthy lifestyles. Accurate diagnosis is essential due to the critical nature and life-threatening risks associated with this disease. Existing diagnostic approaches in cardiovascular disease require enhancement to improve accuracy and precision. Machine learning techniques offer the potential to address these challenges and provide more effective diagnostic and treatment strategies. The limitations of current diagnostic approaches include potential inaccuracies, delays in diagnosis, reliance on subjective interpretation, and generalized treatment guidelines that may not consider individual patient characteristics. So, there is a need for a system for addressing the above challenges. The proposed machine learning-based cardiovascular disease diagnosis and treatment framework utilizes techniques such as mean replacement for handling missing values, synthetic minority oversampling technique (SMOTE) to address data imbalance, and feature importance for effective feature selection. The prediction stage involves an ensemble of logistic regression and K-nearest neighbor (KNN) classifiers to achieve higher accuracy. The performance was analyzed using accuracy, sensitivity, specificity, etc. parameters. The comparative analysis demonstrates that the proposed framework outperforms existing approaches by providing 94.1% accurate predictions with a reduced set of features. The system shows reliability and practical applicability for early diagnosis of cardiovascular diseases and proper treatment in real-world environments. This research highlights the potential of machine learning algorithms in detecting patterns and anomalies in patient data to identify and diagnose cardiovascular diseases. It also emphasizes the optimization of treatment plans and outcome prediction using machine learning, leading to cost reduction and improved patient outcomes.

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A Framework for Effective and Accurate Diagnosis of Cardiovascular Disease and Treatment Using Ensemble Approach of Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Sneha Kumbhar,
  • Kishor Mane

摘要

Cardiovascular diseases (CVDs) have a high mortality rate globally, placing a significant burden on healthcare systems. Improvements in early diagnosis techniques are crucial to address the increasing prevalence of CVDs and their associated risks. CVDs are influenced by various factors such as high blood pressure, family history, stress, age, gender, cholesterol levels, and unhealthy lifestyles. Accurate diagnosis is essential due to the critical nature and life-threatening risks associated with this disease. Existing diagnostic approaches in cardiovascular disease require enhancement to improve accuracy and precision. Machine learning techniques offer the potential to address these challenges and provide more effective diagnostic and treatment strategies. The limitations of current diagnostic approaches include potential inaccuracies, delays in diagnosis, reliance on subjective interpretation, and generalized treatment guidelines that may not consider individual patient characteristics. So, there is a need for a system for addressing the above challenges. The proposed machine learning-based cardiovascular disease diagnosis and treatment framework utilizes techniques such as mean replacement for handling missing values, synthetic minority oversampling technique (SMOTE) to address data imbalance, and feature importance for effective feature selection. The prediction stage involves an ensemble of logistic regression and K-nearest neighbor (KNN) classifiers to achieve higher accuracy. The performance was analyzed using accuracy, sensitivity, specificity, etc. parameters. The comparative analysis demonstrates that the proposed framework outperforms existing approaches by providing 94.1% accurate predictions with a reduced set of features. The system shows reliability and practical applicability for early diagnosis of cardiovascular diseases and proper treatment in real-world environments. This research highlights the potential of machine learning algorithms in detecting patterns and anomalies in patient data to identify and diagnose cardiovascular diseases. It also emphasizes the optimization of treatment plans and outcome prediction using machine learning, leading to cost reduction and improved patient outcomes.