Abstract
Bayes classifier employs a statistical model to categorize data. It is regarded as the most effective classifier when its probabilistic models are able to extract sufficient information from reality. Nevertheless, it is unfeasible to obtain an adequate quantity of data to construct a precise statistical model in certain significant instances. The proposed algorithm aims to enhance the precision of the Bayes statistical model through the incorporation of the Expectation-Maximization (EM) algorithm. The simulation results indicate that the accuracy of the Bayes probabilistic model can be enhanced by approximately 10% under specific conditions. Furthermore, our findings revealed that each medical feature contributes a unique increment to the statistical model.
| Original language | English |
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| Title of host publication | Proceedings - 8th IEEE-EMBS Conference on Biomedical Engineering and Sciences |
| Subtitle of host publication | Healthcare Evolution through Technology and Artificial Intelligence, IECBES 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 226-231 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798350383409 |
| DOIs | |
| State | Published - 2024 |
| Externally published | Yes |
| Event | 8th IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2024 - Penang, Malaysia Duration: 11 12 2024 → 13 12 2024 |
Publication series
| Name | Proceedings - 8th IEEE-EMBS Conference on Biomedical Engineering and Sciences: Healthcare Evolution through Technology and Artificial Intelligence, IECBES 2024 |
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Conference
| Conference | 8th IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2024 |
|---|---|
| Country/Territory | Malaysia |
| City | Penang |
| Period | 11/12/24 → 13/12/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Bayesian Probabilistic Model
- Expectation-Maximization Algorithm
- Medical Features
- Statistical Accuracy Increment