Comparative Analysis of Machine Learning Models for Diabetes Prediction
This paper focuses on analyzing the benchmark Diabetes dataset which consists of eight commonly measured characteristics. The goal of the study is to present comparative analysis of six machine learning models that predict diabetes, as well as various preprocessing techniques (under-over sampling, f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Anhalt University of Applied Sciences
2023-03-01
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| Series: | Proceedings of the International Conference on Applied Innovations in IT |
| Subjects: | |
| Online Access: | https://icaiit.org/paper.php?paper=11th_ICAIIT_1/2_3 |
| _version_ | 1797834644846018560 |
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| author | Zoran Stojanoski Marija Kalendar Hristijan Gjoreski |
| author_facet | Zoran Stojanoski Marija Kalendar Hristijan Gjoreski |
| author_sort | Zoran Stojanoski |
| collection | DOAJ |
| description | This paper focuses on analyzing the benchmark Diabetes dataset which consists of eight commonly measured characteristics. The goal of the study is to present comparative analysis of six machine learning models that predict diabetes, as well as various preprocessing techniques (under-over sampling, feature standardization). The study investigates various approaches and presents results demonstrating that machine learning algorithms can achieve high accuracy results for diabetes prediction, enabling early detection and better outcomes for patients. The paper shows that ensemble learning methods, such as Extra Trees Classifier and Random Forest Classifier, along with appropriate data pre-processing techniques, can lead to 86% accuracy in diabetes prediction classification problems. The paper highlights the potential for machine learning to play a valuable role in the prediction and management of diabetes, leading to improved quality of life and health outcomes for patients. |
| first_indexed | 2024-04-09T14:41:24Z |
| format | Article |
| id | doaj.art-8e7b21a1f2e54a06ba37f148045a2725 |
| institution | Directory Open Access Journal |
| issn | 2199-8876 |
| language | English |
| last_indexed | 2024-04-09T14:41:24Z |
| publishDate | 2023-03-01 |
| publisher | Anhalt University of Applied Sciences |
| record_format | Article |
| series | Proceedings of the International Conference on Applied Innovations in IT |
| spelling | doaj.art-8e7b21a1f2e54a06ba37f148045a27252023-05-03T07:27:16ZengAnhalt University of Applied SciencesProceedings of the International Conference on Applied Innovations in IT2199-88762023-03-01111758010.25673/101916Comparative Analysis of Machine Learning Models for Diabetes PredictionZoran Stojanoski0https://orcid.org/0009-0003-8954-6519Marija Kalendar1https://orcid.org/0000-0002-4226-0690Hristijan Gjoreski2https://orcid.org/0000-0002-0770-4268Computer Technologies and Engineering Department, Faculty of Electrical Engineering and Infomation Technologies, ”SS. Cyril and Methodius University”in Skopje, Rugjer Boshkovikj Str. 18, Skopje, N. MacedoniaComputer Technologies and Engineering Department, Faculty of Electrical Engineering and Infomation Technologies, ”SS. Cyril and Methodius University”in Skopje, Rugjer Boshkovikj Str. 18, Skopje, N. MacedoniaComputer Technologies and Engineering Department, Faculty of Electrical Engineering and Infomation Technologies, ”SS. Cyril and Methodius University”in Skopje, Rugjer Boshkovikj Str. 18, Skopje, N. MacedoniaThis paper focuses on analyzing the benchmark Diabetes dataset which consists of eight commonly measured characteristics. The goal of the study is to present comparative analysis of six machine learning models that predict diabetes, as well as various preprocessing techniques (under-over sampling, feature standardization). The study investigates various approaches and presents results demonstrating that machine learning algorithms can achieve high accuracy results for diabetes prediction, enabling early detection and better outcomes for patients. The paper shows that ensemble learning methods, such as Extra Trees Classifier and Random Forest Classifier, along with appropriate data pre-processing techniques, can lead to 86% accuracy in diabetes prediction classification problems. The paper highlights the potential for machine learning to play a valuable role in the prediction and management of diabetes, leading to improved quality of life and health outcomes for patients.https://icaiit.org/paper.php?paper=11th_ICAIIT_1/2_3machine learningdiabetes predictionfeature analysisml models comparison |
| spellingShingle | Zoran Stojanoski Marija Kalendar Hristijan Gjoreski Comparative Analysis of Machine Learning Models for Diabetes Prediction Proceedings of the International Conference on Applied Innovations in IT machine learning diabetes prediction feature analysis ml models comparison |
| title | Comparative Analysis of Machine Learning Models for Diabetes Prediction |
| title_full | Comparative Analysis of Machine Learning Models for Diabetes Prediction |
| title_fullStr | Comparative Analysis of Machine Learning Models for Diabetes Prediction |
| title_full_unstemmed | Comparative Analysis of Machine Learning Models for Diabetes Prediction |
| title_short | Comparative Analysis of Machine Learning Models for Diabetes Prediction |
| title_sort | comparative analysis of machine learning models for diabetes prediction |
| topic | machine learning diabetes prediction feature analysis ml models comparison |
| url | https://icaiit.org/paper.php?paper=11th_ICAIIT_1/2_3 |
| work_keys_str_mv | AT zoranstojanoski comparativeanalysisofmachinelearningmodelsfordiabetesprediction AT marijakalendar comparativeanalysisofmachinelearningmodelsfordiabetesprediction AT hristijangjoreski comparativeanalysisofmachinelearningmodelsfordiabetesprediction |