Application of machine learning in diabetes prediction based on electronic health record data analysis
With the application of electronic health records (EHRs) in the medical field, the use of machine learning to predict disease has become one of the important research hotspots in the healthcare industry. This study introduces an improved machine learning model specifically designed to predict diabet...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04015.pdf |
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| Summary: | With the application of electronic health records (EHRs) in the medical field, the use of machine learning to predict disease has become one of the important research hotspots in the healthcare industry. This study introduces an improved machine learning model specifically designed to predict diabetes risk, with the aim of improving the accuracy of predictions. The purpose of the study is not only to refine the model, but also to evaluate the performance of the model according to the experimental results. The integrated model was used in this experiment, and the prediction accuracy of diabetes reached 77.7%, showing strong generalization ability on the test data set. These results show that the model performs well at predicting diabetes, but there is still room for further improvement. While presenting the current research results, this study also Outlines future research directions, focusing on further improving the accuracy and reliability of the model. Th is research contributes to the development of machine learning in healthcare, specifically improving disease prediction models through advanced data analysis techniques. |
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| ISSN: | 2271-2097 |