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Fine tuned CatBoost machine learning approach for early detection of cardiovascular disease through predictive
Muhammad Hamid1, Fahima Hajjej2, Ala Saleh Alluhaidan3
1Department of Computer Science, Government College Women University Sialkot, Sialkot, 51310, Pakistan.
Insights
A new CatBoost machine learning model accurately predicts cardiovascular disease (CVD) stages using hospital data. This advanced approach achieves 99% accuracy, aiding early diagnosis and improving patient outcomes.
Area of Science:
- Medical Informatics
- Machine Learning in Healthcare
- Cardiovascular Medicine
Background:
- Cardiovascular disease (CVD) is a major global health concern, necessitating improved diagnostic methods.
- Early detection of CVD is crucial for effective treatment and improved patient survival rates.
- Machine learning (ML) offers promising avenues for predictive modeling in CVD risk assessment.
Purpose of the Study:
- To develop and evaluate an advanced predictive model for classifying cardiovascular disease (CVD) stages.
- To leverage the CatBoost algorithm for enhanced CVD diagnosis using hospital records.
- To improve the accuracy and efficiency of early-stage heart disease detection.
Main Methods:
- Utilized a publicly available hospital records dataset with 12 predictor variables.
- Implemented feature selection, data augmentation, and rigorous validation techniques.
- Evaluated multiple ML algorithms, focusing on a fine-tuned CatBoost model.
Main Results:
- The CatBoost model achieved superior performance in classifying CVD stages.
- Attained an F1-score of 99% and an overall accuracy of 99.02%.
- Demonstrated automated feature selection and effective early-stage heart disease detection.
Conclusions:
- The CatBoost algorithm shows significant potential for rapid and accurate CVD diagnosis.
- The model can effectively support clinical decision-making for cardiovascular conditions.
- Further external validation is planned to confirm generalizability and clinical applicability.
Abstract:
Cardiovascular disease (CVD) remains one of the leading causes of morbidity and mortality worldwide, highlighting the urgent need for early-stage diagnosis to improve clinical outcomes. Machine learning (ML) approaches have demonstrated substantial potential in predictive modeling for CVD risk assessment. In this study, we propose an advanced predictive model based on the CatBoost algorithm to classify various stages of CVD using hospital records as the primary data source. The dataset, sourced from a publicly available repository, comprises 12 key predictor variables. The proposed methodology incorporates feature selection, rigorous validation processes, and data augmentation to enhance predictive performance and address the challenges associated with high-dimensional medical data. Among several ML algorithms evaluated, the fine-tuned CatBoost model achieved the highest performance, automating feature selection and facilitating the detection of early-stage heart disease. The model attained an impressive F1-score of 99% and an overall accuracy of 99.02%, outperforming existing ML-based approaches. These findings underscore the potential of the CatBoost algorithm for rapid and accurate CVD diagnosis, thereby supporting clinical decision-making. Future work will focus on external validation and testing on independent datasets to further assess the model's generalizability and clinical applicability.
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