Comparing Different Machine Learning Techniques in Predicting Diabetes on Early Stage
One of the diseases that is constantly spreading and is estimated to cause a significant number of deaths worldwide is diabetes mellitus. It is determined by the quantity of a blood sugar molecule made from glucose. The possibility of this disease has been predicted using a variety of methods. To fo...
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MDPI AG
2024-03-01
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| author | Shweta Yadu Rashmi Chandra Vivek Kumar Sinha |
| author_facet | Shweta Yadu Rashmi Chandra Vivek Kumar Sinha |
| author_sort | Shweta Yadu |
| collection | DOAJ |
| description | One of the diseases that is constantly spreading and is estimated to cause a significant number of deaths worldwide is diabetes mellitus. It is determined by the quantity of a blood sugar molecule made from glucose. The possibility of this disease has been predicted using a variety of methods. To forecast diabetes at an early stage, adequate and clear data on diabetic individuals are needed. In this study, 520 records from a hospital in Bangladesh with 16 different characteristic numbers were used to make predictions. At UCI, this dataset is accessible to everyone. We used Random Forest, Ada Booster, KNN, and Bagging algorithms after feature selection. Through 10-fold cross-validation, it was discovered that the Random Forest method had the best test accuracy, scoring 97.03% correctly and 95.03% correctly. |
| format | Article |
| id | doaj-art-6b2b564f99204188bde560ef476cfddc |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-6b2b564f99204188bde560ef476cfddc2025-08-20T01:55:31ZengMDPI AGEngineering Proceedings2673-45912024-03-016212010.3390/engproc2024062020Comparing Different Machine Learning Techniques in Predicting Diabetes on Early StageShweta Yadu0Rashmi Chandra1Vivek Kumar Sinha2Department of Computer Science Engineering, Raipur Institute of Technology, Raipur 492101, Chhattisgarh, IndiaDepartment of Computer Science and Engineering, Chhattisgarh Swami Vivekanand Technical University, Newai, Bhilai 491107, Chhattisgarh, IndiaDepartment of Computer Science and Engineering, Noida International University, Greater Noida 203201, Uttar Pradesh, IndiaOne of the diseases that is constantly spreading and is estimated to cause a significant number of deaths worldwide is diabetes mellitus. It is determined by the quantity of a blood sugar molecule made from glucose. The possibility of this disease has been predicted using a variety of methods. To forecast diabetes at an early stage, adequate and clear data on diabetic individuals are needed. In this study, 520 records from a hospital in Bangladesh with 16 different characteristic numbers were used to make predictions. At UCI, this dataset is accessible to everyone. We used Random Forest, Ada Booster, KNN, and Bagging algorithms after feature selection. Through 10-fold cross-validation, it was discovered that the Random Forest method had the best test accuracy, scoring 97.03% correctly and 95.03% correctly.https://www.mdpi.com/2673-4591/62/1/20Ada boostbaggingKNNmachine learningpredictive analysisrandom forest |
| spellingShingle | Shweta Yadu Rashmi Chandra Vivek Kumar Sinha Comparing Different Machine Learning Techniques in Predicting Diabetes on Early Stage Engineering Proceedings Ada boost bagging KNN machine learning predictive analysis random forest |
| title | Comparing Different Machine Learning Techniques in Predicting Diabetes on Early Stage |
| title_full | Comparing Different Machine Learning Techniques in Predicting Diabetes on Early Stage |
| title_fullStr | Comparing Different Machine Learning Techniques in Predicting Diabetes on Early Stage |
| title_full_unstemmed | Comparing Different Machine Learning Techniques in Predicting Diabetes on Early Stage |
| title_short | Comparing Different Machine Learning Techniques in Predicting Diabetes on Early Stage |
| title_sort | comparing different machine learning techniques in predicting diabetes on early stage |
| topic | Ada boost bagging KNN machine learning predictive analysis random forest |
| url | https://www.mdpi.com/2673-4591/62/1/20 |
| work_keys_str_mv | AT shwetayadu comparingdifferentmachinelearningtechniquesinpredictingdiabetesonearlystage AT rashmichandra comparingdifferentmachinelearningtechniquesinpredictingdiabetesonearlystage AT vivekkumarsinha comparingdifferentmachinelearningtechniquesinpredictingdiabetesonearlystage |