Enhancing Multiple Disease Prediction Systems Using Advanced Machine Learning Algorithms: A Web-Based Approach
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| _version_ | 1866902266068336640 |
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| author | Anthony, Eniola Olayori |
| author_facet | Anthony, Eniola Olayori |
| contents | <p><span>This research explores the utilization of advanced machine learning algorithms to enhance disease prediction accuracy and its usability in healthcare systems.</span></p> <p><span>It builds on existing multiple disease prediction systems with a focus on optimising the prediction of heart diseases using advanced algorithms</span><span>.<br><br></span><span>The methodology used optimised architectures such as Multi-Layer Perceptron (MLP), Gradient Boosting and AdaBoost classifiers, a well-structured feature engineering process was carried out, and the heart disease prediction model for instance had excellent performance metrics, achieving an accuracy of 91.80%, sensitivity of 93.94%, and a strong f1-score of 91.18%.</span></p> <p><span>The research extended beyond the improvement of Algorithms of an existing paper, it also includes a modern web-based interface that was built using Python and Streamlit. </span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15122623 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Enhancing Multiple Disease Prediction Systems Using Advanced Machine Learning Algorithms: A Web-Based Approach Anthony, Eniola Olayori disease prediction heart diseases machine learning algorithms healthcare systems <p><span>This research explores the utilization of advanced machine learning algorithms to enhance disease prediction accuracy and its usability in healthcare systems.</span></p> <p><span>It builds on existing multiple disease prediction systems with a focus on optimising the prediction of heart diseases using advanced algorithms</span><span>.<br><br></span><span>The methodology used optimised architectures such as Multi-Layer Perceptron (MLP), Gradient Boosting and AdaBoost classifiers, a well-structured feature engineering process was carried out, and the heart disease prediction model for instance had excellent performance metrics, achieving an accuracy of 91.80%, sensitivity of 93.94%, and a strong f1-score of 91.18%.</span></p> <p><span>The research extended beyond the improvement of Algorithms of an existing paper, it also includes a modern web-based interface that was built using Python and Streamlit. </span></p> |
| title | Enhancing Multiple Disease Prediction Systems Using Advanced Machine Learning Algorithms: A Web-Based Approach |
| topic | disease prediction heart diseases machine learning algorithms healthcare systems |
| url | https://doi.org/10.5281/zenodo.15122623 |