Machine Learning Approach to Predict Cardiovascular Disease in Bangladesh: Evidence from a Cross-Sectional Study in 2023. Article Swipe
Sorif Hossain
,
Mohammad Kamrul Hasan
,
Mohammad Omar Faruk
,
Nelufa Aktar
,
Riyadh Hossain
,
Kabir Hossain
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3667056/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3667056/v1
Background Cardiovascular disorders (CVDs) are widely considered the leading cause of death worldwide. Lower and middle-income countries (LMICs) like Bangladesh are also affected by several types of CVDs such as heart failure and stroke. The leading factors of death in Bangladesh have increasingly switched from severe infections and parasitic illness to CVDs recently. Materials and methods The study dataset is a random sample of the 391 CVD patients' medical records collected between August 2022 and April 2023 using simple random sampling. Moreover, 260 data are also collected from individuals with no CVD problem for comparison purposes. Crosstabs and chi-square are used to find the association between CVD and explanatory variables. Logistic regression, Naïve Bayes classifier, Decision Tree, AdaBoost classifier, Random Forest, Bagging Tree, and Ensemble learning classifiers are used to predict CVD in this study. The performance evaluations encompassed accuracy, sensitivity, specificity, and the area under the receiver operator characteristic (AU-ROC) curve. Result Random Forest has the highest precision among the five techniques considered. The precision rates for the mentioned classifiers are as follows: Logistic Regression (93.67%), Naïve Bayes (94.87%), Decision Tree (96.1%), AdaBoost (94.94%), Random Forest (96.15%), and Bagging Tree (94.87%). The Random Forest classifier maintains the highest balance between correct and incorrect predictions. With 98.04% accuracy, the Random Forest Classifier achieves the best precision (96.15%), robust recall (100%), and a high F1 score (97.7%). In contrast, the Logistic Regression model achieves the lowest accuracy at 95.42%. Remarkably, the Random Forest classifier attains the highest AUC value (0.989). Conclusion This research is mainly focused on identifying factors that are critical in impacting CVD patients and predicting CVD risk. It is strongly advised that the Random Forest technique be implemented in the system for predicting cardiac disease. This research may change clinical practice by giving doctors a new instrument to determine a patient's prognosis for CVD.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3667056/v1
- https://www.researchsquare.com/article/rs-3667056/latest.pdf
- OA Status
- gold
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389242097
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https://openalex.org/W4389242097Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-3667056/v1Digital Object Identifier
- Title
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Machine Learning Approach to Predict Cardiovascular Disease in Bangladesh: Evidence from a Cross-Sectional Study in 2023.Work title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-01Full publication date if available
- Authors
-
Sorif Hossain, Mohammad Kamrul Hasan, Mohammad Omar Faruk, Nelufa Aktar, Riyadh Hossain, Kabir HossainList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-3667056/v1Publisher landing page
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https://www.researchsquare.com/article/rs-3667056/latest.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-3667056/latest.pdfDirect OA link when available
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Cross-sectional study, Disease, Environmental health, Medicine, Artificial intelligence, Computer science, Internal medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.using | 78 |
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| abstract_inverted_index.robust | 218 |
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| abstract_inverted_index.severe | 46 |
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| abstract_inverted_index.Forest, | 121 |
| abstract_inverted_index.advised | 273 |
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| abstract_inverted_index.between | 72, 106, 201 |
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| abstract_inverted_index.dataset | 59 |
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| abstract_inverted_index.problem | 93 |
| abstract_inverted_index.records | 70 |
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| abstract_inverted_index.stroke. | 34 |
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| abstract_inverted_index.(97.7%). | 226 |
| abstract_inverted_index.(AU-ROC) | 151 |
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| abstract_inverted_index.(94.87%). | 192 |
| abstract_inverted_index.(94.94%), | 185 |
| abstract_inverted_index.(96.15%), | 188, 217 |
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| abstract_inverted_index.Materials | 54 |
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| abstract_inverted_index.contrast, | 228 |
| abstract_inverted_index.countries | 17 |
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| abstract_inverted_index.disorders | 3 |
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| abstract_inverted_index.mentioned | 170 |
| abstract_inverted_index.parasitic | 49 |
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| abstract_inverted_index.patients' | 68 |
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| abstract_inverted_index.recently. | 53 |
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| abstract_inverted_index.technique | 278 |
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| abstract_inverted_index.Regression | 176, 231 |
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| institutions_distinct_count | 6 |
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