Stacking Model for Heart Stroke Prediction using Machine Learning Techniques Article Swipe
Subasish Mohapatra
,
Indrani Mishra
,
Subhadarshini Mohanty
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.4108/eetpht.9.4057
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.4108/eetpht.9.4057
The paper presents an adaptive model that utilized the machine learning algorithms to predict the heart diseases. As heart disease is one of the leading causes of death and understanding its mechanism, effective prevention, diagnosis, and treatment is very crucial. With the help of data analytics, machine learning, artificial intelligence, it is possible to provide optimal solution to the heart diseases. But still getting optimal accuracy is a challenging issue. Identifying the data pattern, correlation and algorithms affects the accuracy very much. In this work, a stacking model has been proposed to find the best models out of it and validate the model for better prediction accuracy. The model is stacked with seven algorithms different machine learning algorithms such as Radom Forest, Naïve Bayes, Linear Regression, Decision Tree, Ad boost, K Nearest Neighbour, and Gradient Boosting. The experiment was carried out with a training and testing ration of 80:20 in ration. Evaluations are carried out in different measures such as Precision, Recall, F Score, and Accuracy to demonstrate the efficiency of the algorithms. Form the experimentation it is observed that the gradient boosting outperforms the other competitive approaches as this algorithm combines weak predictive models to form a stronger ensemble model that can make highly accurate predictions with an accuracy of 94.67 percentages.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4108/eetpht.9.4057
- https://publications.eai.eu/index.php/phat/article/download/4057/2597
- OA Status
- diamond
- Cited By
- 3
- References
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387311202
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387311202Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.4108/eetpht.9.4057Digital Object Identifier
- Title
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Stacking Model for Heart Stroke Prediction using Machine Learning TechniquesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-03Full publication date if available
- Authors
-
Subasish Mohapatra, Indrani Mishra, Subhadarshini MohantyList of authors in order
- Landing page
-
https://doi.org/10.4108/eetpht.9.4057Publisher landing page
- PDF URL
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https://publications.eai.eu/index.php/phat/article/download/4057/2597Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://publications.eai.eu/index.php/phat/article/download/4057/2597Direct OA link when available
- Concepts
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Machine learning, Decision tree, Gradient boosting, Artificial intelligence, Computer science, Boosting (machine learning), Naive Bayes classifier, Ensemble learning, AdaBoost, Random forest, Support vector machine, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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5Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Tree, | 127 |
| abstract_inverted_index.death | 27 |
| abstract_inverted_index.heart | 15, 18, 59 |
| abstract_inverted_index.model | 5, 87, 102, 108, 200 |
| abstract_inverted_index.much. | 81 |
| abstract_inverted_index.other | 185 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.seven | 112 |
| abstract_inverted_index.still | 62 |
| abstract_inverted_index.work, | 84 |
| abstract_inverted_index.Bayes, | 123 |
| abstract_inverted_index.Linear | 124 |
| abstract_inverted_index.Naïve | 122 |
| abstract_inverted_index.Score, | 163 |
| abstract_inverted_index.better | 104 |
| abstract_inverted_index.boost, | 129 |
| abstract_inverted_index.causes | 25 |
| abstract_inverted_index.highly | 204 |
| abstract_inverted_index.issue. | 69 |
| abstract_inverted_index.models | 95, 194 |
| abstract_inverted_index.ration | 146 |
| abstract_inverted_index.Forest, | 121 |
| abstract_inverted_index.Nearest | 131 |
| abstract_inverted_index.Recall, | 161 |
| abstract_inverted_index.affects | 77 |
| abstract_inverted_index.carried | 139, 153 |
| abstract_inverted_index.disease | 19 |
| abstract_inverted_index.getting | 63 |
| abstract_inverted_index.leading | 24 |
| abstract_inverted_index.machine | 9, 46, 115 |
| abstract_inverted_index.optimal | 55, 64 |
| abstract_inverted_index.predict | 13 |
| abstract_inverted_index.provide | 54 |
| abstract_inverted_index.ration. | 150 |
| abstract_inverted_index.stacked | 110 |
| abstract_inverted_index.testing | 145 |
| abstract_inverted_index.Accuracy | 165 |
| abstract_inverted_index.Decision | 126 |
| abstract_inverted_index.Gradient | 134 |
| abstract_inverted_index.accuracy | 65, 79, 209 |
| abstract_inverted_index.accurate | 205 |
| abstract_inverted_index.adaptive | 4 |
| abstract_inverted_index.boosting | 182 |
| abstract_inverted_index.combines | 191 |
| abstract_inverted_index.crucial. | 39 |
| abstract_inverted_index.ensemble | 199 |
| abstract_inverted_index.gradient | 181 |
| abstract_inverted_index.learning | 10, 116 |
| abstract_inverted_index.measures | 157 |
| abstract_inverted_index.observed | 178 |
| abstract_inverted_index.pattern, | 73 |
| abstract_inverted_index.possible | 52 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.proposed | 90 |
| abstract_inverted_index.solution | 56 |
| abstract_inverted_index.stacking | 86 |
| abstract_inverted_index.stronger | 198 |
| abstract_inverted_index.training | 143 |
| abstract_inverted_index.utilized | 7 |
| abstract_inverted_index.validate | 100 |
| abstract_inverted_index.Boosting. | 135 |
| abstract_inverted_index.accuracy. | 106 |
| abstract_inverted_index.algorithm | 190 |
| abstract_inverted_index.different | 114, 156 |
| abstract_inverted_index.diseases. | 16, 60 |
| abstract_inverted_index.effective | 32 |
| abstract_inverted_index.learning, | 47 |
| abstract_inverted_index.treatment | 36 |
| abstract_inverted_index.Neighbour, | 132 |
| abstract_inverted_index.Precision, | 160 |
| abstract_inverted_index.algorithms | 11, 76, 113, 117 |
| abstract_inverted_index.analytics, | 45 |
| abstract_inverted_index.approaches | 187 |
| abstract_inverted_index.artificial | 48 |
| abstract_inverted_index.diagnosis, | 34 |
| abstract_inverted_index.efficiency | 169 |
| abstract_inverted_index.experiment | 137 |
| abstract_inverted_index.mechanism, | 31 |
| abstract_inverted_index.prediction | 105 |
| abstract_inverted_index.predictive | 193 |
| abstract_inverted_index.Evaluations | 151 |
| abstract_inverted_index.Identifying | 70 |
| abstract_inverted_index.Regression, | 125 |
| abstract_inverted_index.algorithms. | 172 |
| abstract_inverted_index.challenging | 68 |
| abstract_inverted_index.competitive | 186 |
| abstract_inverted_index.correlation | 74 |
| abstract_inverted_index.demonstrate | 167 |
| abstract_inverted_index.outperforms | 183 |
| abstract_inverted_index.predictions | 206 |
| abstract_inverted_index.prevention, | 33 |
| abstract_inverted_index.percentages. | 212 |
| abstract_inverted_index.intelligence, | 49 |
| abstract_inverted_index.understanding | 29 |
| abstract_inverted_index.experimentation | 175 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.85236023 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |