Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910460320677888 |
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| author | K, Dayana Nandini, S. R, Sanjjushri Varshini |
| author_facet | K, Dayana Nandini, S. R, Sanjjushri Varshini |
| contents | The detection of cardiovascular diseases (CVD) using machine learning techniques represents a significant advancement in medical diagnostics, aiming to enhance early detection, accuracy, and efficiency. This study explores a comparative analysis of various machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. By utilising a structured workflow encompassing data collection, preprocessing, model selection and hyperparameter tuning, training, evaluation, and choice of the optimal model, this research addresses the critical need for improved diagnostic tools. The findings highlight the efficacy of ensemble methods and advanced algorithms in providing reliable predictions, thereby offering a comprehensive framework for CVD detection that can be readily implemented and adapted in clinical settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17059 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases K, Dayana Nandini, S. R, Sanjjushri Varshini Machine Learning The detection of cardiovascular diseases (CVD) using machine learning techniques represents a significant advancement in medical diagnostics, aiming to enhance early detection, accuracy, and efficiency. This study explores a comparative analysis of various machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. By utilising a structured workflow encompassing data collection, preprocessing, model selection and hyperparameter tuning, training, evaluation, and choice of the optimal model, this research addresses the critical need for improved diagnostic tools. The findings highlight the efficacy of ensemble methods and advanced algorithms in providing reliable predictions, thereby offering a comprehensive framework for CVD detection that can be readily implemented and adapted in clinical settings. |
| title | Comparative Study of Machine Learning Algorithms in Detecting Cardiovascular Diseases |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.17059 |