| Issue |
E3S Web Conf.
Volume 399, 2023
International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
|
|
|---|---|---|
| Article Number | 04019 | |
| Number of page(s) | 9 | |
| Section | Computer Science | |
| DOI | https://doi.org/10.1051/e3sconf/202339904019 | |
| Published online | 12 July 2023 | |
Accurate Prediction of Myocardial Infarction By Comparing Logistic Regression Algorithm with CatBoost Classifier
1 Research Scholar, Department of Computer Science Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India, Pincode: 602105
2 Research Guide, Corresponding Author, Department of Computer Science Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India, Pincode: 602105
* Corresponding author: thangarajssecse@gmail.com
Aim: The forecast of Myocardial Infarction for humans employing a Machine learning model by corresponding a Logistic Regression Algorithm with a CatBoost Classifier. The accuracy is enhanced by utilizing the novel LR Classifier. Materials and Methods: The study utilized a total of 20 sample iterations, with 10 samples per group. Group 1 was analyzed using a logistic regression algorithm, while Group 2 was analyzed using a decision tree classifier. The statistical power was set at 80%, and the confidence level was set at 95%. Results: The accuracy of the outcome with logistic regression is 94.61% and CatBoost Classifier is 79.516%, both the groups are statistically significant as p = 0.015 (<0.05) is the significant value in the independent sample T-test between LR and CB Classifier. Conclusion: This research concludes that the logistic regression algorithm gives the most accurate mortality with the difference of 15.1%, compared to the CatBoost Classifier.
Key words: Novel LR Classifier / Logistic Regression Algorithm / CatBoost Classifier / Myocardial Infarction / Heart Attack / Predictions / Heart / Health
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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