Detection and prediction of diabetes using effective biomarkers
| Autor: | Mohammad Ehsan Farnoodian, Mohammad Karimi Moridani, Hanieh Mokhber |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol 12, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 21681163 2168-1171 2168-1163 |
| DOI: | 10.1080/21681163.2023.2264937 |
| Popis: | Diabetes is a prevalent and costly condition, with early diagnosis pivotal in mitigating its progression and complications. The diagnostic process often contends with data ambiguity and decision uncertainty, adding complexity to achieving definitive outcomes. This study addresses the diabetes diagnostic challenge through data mining and machine learning techniques. It involves training various machine learning algorithms and conducting statistical analysis on a dataset comprising 520 patients, encompassing both normal and diabetic cases, to discern influential features. Incorporating 17 features as classifier inputs, this research evaluates the diagnostic performance using four reputable techniques: support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP), and k-nearest neighbor (kNN). The outcomes underscore the SVM model's superior performance, boasting accuracy, specificity, and sensitivity values of 98.78±1.96%, 99.28±1.63%, and 97.32±2.45%, respectively, across 50 iterations. The findings establish SVM as the preferred method for diabetes diagnosis. This study highlights the efficacy of data mining and machine learning models in diabetes diagnosis. While these methods exhibit respectable predictive accuracy, their integration with a physician's assessment promises even better patient outcomes. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|