An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
Stroke is a major public health issue with significant economic consequences. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. We tackle the overlooked...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/16/9/417 |
| _version_ | 1797581680503947264 |
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| author | Ivan G. Ivanov Yordan Kumchev Vincent James Hooper |
| author_facet | Ivan G. Ivanov Yordan Kumchev Vincent James Hooper |
| author_sort | Ivan G. Ivanov |
| collection | DOAJ |
| description | Stroke is a major public health issue with significant economic consequences. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Our study focuses on predicting stroke in a general context rather than specific subtypes. This clarification will not only ensure a clear understanding of our study’s scope but also enhance the overall transparency and impact of our findings. We construct an optimization model and describe an effective methodology and algorithms for machine learning classification, accommodating missing data and imbalances. Our models outperform previous efforts in stroke prediction, demonstrating higher sensitivity, specificity, accuracy, and precision. Data quality and preprocessing play a crucial role in developing reliable models. The proposed algorithm using SVMs achieves 98% accuracy and 97% recall score. In-depth data analysis and advanced machine learning techniques improve stroke prediction. This research highlights the value of data-oriented approaches, leading to enhanced accuracy and understanding of stroke risk factors. These methods can be applied to other medical domains, benefiting patient care and public health outcomes. By incorporating our findings, the efficiency and effectiveness of the public health system can be improved. |
| first_indexed | 2024-03-10T23:08:01Z |
| format | Article |
| id | doaj.art-e8103c366a9f4915af479472156b0766 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T23:08:01Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-e8103c366a9f4915af479472156b07662023-11-19T09:12:51ZengMDPI AGAlgorithms1999-48932023-09-0116941710.3390/a16090417An Optimization Precise Model of Stroke Data to Improve Stroke PredictionIvan G. Ivanov0Yordan Kumchev1Vincent James Hooper2Faculty of Economics and Business Administration, Sofia University “St.Kl.Ohridski”, 1000 Sofia, BulgariaFaculty of Economics and Social Sciences, Plovdiv University Paisii Hilendarski, 4000 Plovdiv, BulgariaCollege of Business Administration, Prince Mohammad Bin Fahd University, Al Khobar 31952, Saudi ArabiaStroke is a major public health issue with significant economic consequences. This study aims to enhance stroke prediction by addressing imbalanced datasets and algorithmic bias. Our research focuses on accurately and precisely detecting stroke possibility to aid prevention. We tackle the overlooked aspect of imbalanced datasets in the healthcare literature. Our study focuses on predicting stroke in a general context rather than specific subtypes. This clarification will not only ensure a clear understanding of our study’s scope but also enhance the overall transparency and impact of our findings. We construct an optimization model and describe an effective methodology and algorithms for machine learning classification, accommodating missing data and imbalances. Our models outperform previous efforts in stroke prediction, demonstrating higher sensitivity, specificity, accuracy, and precision. Data quality and preprocessing play a crucial role in developing reliable models. The proposed algorithm using SVMs achieves 98% accuracy and 97% recall score. In-depth data analysis and advanced machine learning techniques improve stroke prediction. This research highlights the value of data-oriented approaches, leading to enhanced accuracy and understanding of stroke risk factors. These methods can be applied to other medical domains, benefiting patient care and public health outcomes. By incorporating our findings, the efficiency and effectiveness of the public health system can be improved.https://www.mdpi.com/1999-4893/16/9/417data analyticsmachine learning modelingclassification modelsresample procedureconfusion matrixPython |
| spellingShingle | Ivan G. Ivanov Yordan Kumchev Vincent James Hooper An Optimization Precise Model of Stroke Data to Improve Stroke Prediction Algorithms data analytics machine learning modeling classification models resample procedure confusion matrix Python |
| title | An Optimization Precise Model of Stroke Data to Improve Stroke Prediction |
| title_full | An Optimization Precise Model of Stroke Data to Improve Stroke Prediction |
| title_fullStr | An Optimization Precise Model of Stroke Data to Improve Stroke Prediction |
| title_full_unstemmed | An Optimization Precise Model of Stroke Data to Improve Stroke Prediction |
| title_short | An Optimization Precise Model of Stroke Data to Improve Stroke Prediction |
| title_sort | optimization precise model of stroke data to improve stroke prediction |
| topic | data analytics machine learning modeling classification models resample procedure confusion matrix Python |
| url | https://www.mdpi.com/1999-4893/16/9/417 |
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