Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models
Diabetes poses a significant threat to global health, making accurate prediction and effective treatment of the disease critical. This study explores the application of machine learning algorithms in assessing diabetes risk, with a particular focus on Decision Trees (DT) and Ensemble Learning techni...
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| Format: | Article |
| Language: | English |
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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_02020.pdf |
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| _version_ | 1825206549201551360 |
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| author | Lei Tianxing |
| author_facet | Lei Tianxing |
| author_sort | Lei Tianxing |
| collection | DOAJ |
| description | Diabetes poses a significant threat to global health, making accurate prediction and effective treatment of the disease critical. This study explores the application of machine learning algorithms in assessing diabetes risk, with a particular focus on Decision Trees (DT) and Ensemble Learning techniques. DT methodically evaluate various indicators that impact classification outcomes, using sequential decisions to classify each indicator based on the results of previous classifications. This process ensures that all possible combinations of indicators are mapped to a single classification result. Ensemble Learning, on the other hand, leverages multiple classifiers with assigned weights to form a robust ensemble. Each classifier provides its prediction, and the final classification result is derived from a weighted voting mechanism based on the performance of each learner. The study’s experimental results demonstrate that applying Principal Component Analysis (PCA) to preprocess the data, followed by training a Random Forest (RF) model with 80% of the dataset, achieves an impressive accuracy of 89.86%. This high accuracy highlights the effectiveness of machine learning algorithms in predicting diabetes risk. The findings underscore the potential of these methods in enhancing diabetes management and offer a valuable contribution to the field of medical predictive analytics. |
| format | Article |
| id | doaj-art-2617b120dc5646e9a879944215ee31bc |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-2617b120dc5646e9a879944215ee31bc2025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700202010.1051/itmconf/20257002020itmconf_dai2024_02020Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning ModelsLei Tianxing0School of Information Science and Engineering, Yunnan UniversityDiabetes poses a significant threat to global health, making accurate prediction and effective treatment of the disease critical. This study explores the application of machine learning algorithms in assessing diabetes risk, with a particular focus on Decision Trees (DT) and Ensemble Learning techniques. DT methodically evaluate various indicators that impact classification outcomes, using sequential decisions to classify each indicator based on the results of previous classifications. This process ensures that all possible combinations of indicators are mapped to a single classification result. Ensemble Learning, on the other hand, leverages multiple classifiers with assigned weights to form a robust ensemble. Each classifier provides its prediction, and the final classification result is derived from a weighted voting mechanism based on the performance of each learner. The study’s experimental results demonstrate that applying Principal Component Analysis (PCA) to preprocess the data, followed by training a Random Forest (RF) model with 80% of the dataset, achieves an impressive accuracy of 89.86%. This high accuracy highlights the effectiveness of machine learning algorithms in predicting diabetes risk. The findings underscore the potential of these methods in enhancing diabetes management and offer a valuable contribution to the field of medical predictive analytics.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02020.pdf |
| spellingShingle | Lei Tianxing Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models ITM Web of Conferences |
| title | Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models |
| title_full | Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models |
| title_fullStr | Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models |
| title_full_unstemmed | Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models |
| title_short | Diabetes Risk Assessment: A Comparative Study of Decision Trees and Ensemble Learning Models |
| title_sort | diabetes risk assessment a comparative study of decision trees and ensemble learning models |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02020.pdf |
| work_keys_str_mv | AT leitianxing diabetesriskassessmentacomparativestudyofdecisiontreesandensemblelearningmodels |