Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914515586646016 |
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| author | Khokhar, Pir Bakhsh Gravino, Carmine Palomba, Fabio |
| author_facet | Khokhar, Pir Bakhsh Gravino, Carmine Palomba, Fabio |
| contents | This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy Screening program, REPLACE-BG, National Health and Nutrition Examination Survey, and Pima Indians Diabetes Database. The review assesses the performance of ML algorithms like CNN, SVM, Logistic Regression, and XGBoost in predicting diabetes outcomes. The study emphasizes the importance of interdisciplinary collaboration and ethical considerations in ML-based diabetes prediction models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14736 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review Khokhar, Pir Bakhsh Gravino, Carmine Palomba, Fabio Software Engineering Artificial Intelligence This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy Screening program, REPLACE-BG, National Health and Nutrition Examination Survey, and Pima Indians Diabetes Database. The review assesses the performance of ML algorithms like CNN, SVM, Logistic Regression, and XGBoost in predicting diabetes outcomes. The study emphasizes the importance of interdisciplinary collaboration and ethical considerations in ML-based diabetes prediction models. |
| title | Advances in Artificial Intelligence forDiabetes Prediction: Insights from a Systematic Literature Review |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2412.14736 |