Comparisons of Machine Learning Models for Prediction of Susceptibility to Diabetes
Diabetes is a chronic disorder causing millions of people to suffer from severe complications such as heart attacks, kidney failures, and permanent vision loss. This study aims to find an optimal choice among the five selected models that perform the best on diabetes prediction, and thus provide val...
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| 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_04035.pdf |
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| _version_ | 1825206601606234112 |
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| author | Jiao Yutian |
| author_facet | Jiao Yutian |
| author_sort | Jiao Yutian |
| collection | DOAJ |
| description | Diabetes is a chronic disorder causing millions of people to suffer from severe complications such as heart attacks, kidney failures, and permanent vision loss. This study aims to find an optimal choice among the five selected models that perform the best on diabetes prediction, and thus provide valuable insights in early detection of diabetes. This study compares the predictive performance of machine learning models such as Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). The study preprocessed the Pima Indians Diabetes (PID) dataset, and the models were trained on it before being assessed using four assessment criteria. According to the results, LR had the best accuracy of 0.76, with RF and SVM coming in second and third, respectively. Results showed that LR achieved the highest accuracy of 0.76, closely followed by RF and SVM. While SVM has the highest precision, it performs poorly on recall, limiting its overall performance on diabetes prediction. On the contrary, LR and RF achieved good results in the F-score, making them outperform the other models in terms of overall performance score in predicting diabetes. |
| format | Article |
| id | doaj-art-e786961e7aa54be2a1a20b96f5ba37ca |
| 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-e786961e7aa54be2a1a20b96f5ba37ca2025-02-07T08:21:11ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700403510.1051/itmconf/20257004035itmconf_dai2024_04035Comparisons of Machine Learning Models for Prediction of Susceptibility to DiabetesJiao Yutian0Department of Mathematical Sciences, Carnegie Mellon UniversityDiabetes is a chronic disorder causing millions of people to suffer from severe complications such as heart attacks, kidney failures, and permanent vision loss. This study aims to find an optimal choice among the five selected models that perform the best on diabetes prediction, and thus provide valuable insights in early detection of diabetes. This study compares the predictive performance of machine learning models such as Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). The study preprocessed the Pima Indians Diabetes (PID) dataset, and the models were trained on it before being assessed using four assessment criteria. According to the results, LR had the best accuracy of 0.76, with RF and SVM coming in second and third, respectively. Results showed that LR achieved the highest accuracy of 0.76, closely followed by RF and SVM. While SVM has the highest precision, it performs poorly on recall, limiting its overall performance on diabetes prediction. On the contrary, LR and RF achieved good results in the F-score, making them outperform the other models in terms of overall performance score in predicting diabetes.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04035.pdf |
| spellingShingle | Jiao Yutian Comparisons of Machine Learning Models for Prediction of Susceptibility to Diabetes ITM Web of Conferences |
| title | Comparisons of Machine Learning Models for Prediction of Susceptibility to Diabetes |
| title_full | Comparisons of Machine Learning Models for Prediction of Susceptibility to Diabetes |
| title_fullStr | Comparisons of Machine Learning Models for Prediction of Susceptibility to Diabetes |
| title_full_unstemmed | Comparisons of Machine Learning Models for Prediction of Susceptibility to Diabetes |
| title_short | Comparisons of Machine Learning Models for Prediction of Susceptibility to Diabetes |
| title_sort | comparisons of machine learning models for prediction of susceptibility to diabetes |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_04035.pdf |
| work_keys_str_mv | AT jiaoyutian comparisonsofmachinelearningmodelsforpredictionofsusceptibilitytodiabetes |