Cardiovascular Disease Prediction using Recursive Feature Elimination
and Gradient Boosting Classification Techniques
- URL: http://arxiv.org/abs/2106.08889v1
- Date: Fri, 11 Jun 2021 16:17:42 GMT
- Title: Cardiovascular Disease Prediction using Recursive Feature Elimination
and Gradient Boosting Classification Techniques
- Authors: Prasannavenkatesan Theerthagiri, Vidya J
- Abstract summary: This paper proposes a proposed gradient boosting (RFE-GB) algorithm in order to obtain accurate heart disease prediction.
The patients health record with important CVD features has been analyzed for the evaluation of the results.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Cardiovascular diseases (CVDs) are one of the most common chronic illnesses
that affect peoples health. Early detection of CVDs can reduce mortality rates
by preventing or reducing the severity of the disease. Machine learning
algorithms are a promising method for identifying risk factors. This paper
proposes a proposed recursive feature elimination-based gradient boosting
(RFE-GB) algorithm in order to obtain accurate heart disease prediction. The
patients health record with important CVD features has been analyzed for the
evaluation of the results. Several other machine learning methods were also
used to build the prediction model, and the results were compared with the
proposed model. The results of this proposed model infer that the combined
recursive feature elimination and gradient boosting algorithm achieves the
highest accuracy (89.7 %). Further, with an area under the curve of 0.84, the
proposed RFE-GB algorithm was found superior and had obtained a substantial
gain over other techniques. Thus, the proposed RFE-GB algorithm will serve as a
prominent model for CVD estimation and treatment.
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