Comparison of Machine Learning Algorithms for Predicting Thyroid Disorders in Diabetic Patients
Uloženo v:
| Vydáno v: | Informatica vol. 49, no. 12 (Feb 2025), p. 105 |
|---|---|
| Hlavní autor: | |
| Další autoři: | , |
| Vydáno: |
Slovenian Society Informatika / Slovensko drustvo Informatika
|
| Témata: | |
| On-line přístup: | Citation/Abstract Full Text Full Text - PDF |
| Tagy: |
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3188467301 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0350-5596 | ||
| 022 | |a 1854-3871 | ||
| 024 | 7 | |a 10.31449/inf.v49il2.6927 |2 doi | |
| 035 | |a 3188467301 | ||
| 045 | 2 | |b d20250201 |b d20250228 | |
| 084 | |a 179436 |2 nlm | ||
| 100 | 1 | |a Sayyid, Hiba O |u Department of Computer Science, University of Basrah, College of Computer Sciences and Information Technology, Basrah, Iraq | |
| 245 | 1 | |a Comparison of Machine Learning Algorithms for Predicting Thyroid Disorders in Diabetic Patients | |
| 260 | |b Slovenian Society Informatika / Slovensko drustvo Informatika |c Feb 2025 | ||
| 513 | |a Feature | ||
| 520 | 3 | |a Machine Learning (ML), a sub field of Artificial Intelligence (AL), has been used successfully in the healthcare domain for disease diagnosis. Thyroid disorders and diabetes are two of the most prevalent and interconnected chronic diseases, as both play critical roles in regulating various physiological processes in the body. This study aims to predict thyroid disorders in diabetes patients using six machine learning algorithms: Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM). A locally sourced dataset comprising 44,539 instances of diabetic patients was utilized, undergoing preprocessing steps including data cleaning, encoding, and balancing. Two balancing techniques were employed: manual balancing andRandomUnderSampler. The dataset was partitioned into training and testing sets using a Stratified K-Fold cross-validation approach with 10 folds to ensure robust evaluation. Each algorithm's performance was assessed using metrics such as accuracy and Fl-score. Among the models, the RF algorithm outperformed the others, achieving the highest accuracy of 95% on the manually balanced dataset and 84% when the RandomUnderSampler technique was employed. Additionally, the Fl-scores for RF were 95% and 82%, respectively, indicating its robustness in handling imbalanced datasets. This study highlights the importance of selecting appropriate preprocessing techniques and machine learning methods for healthcare datasets. The findings can assist healthcare providers in making early diagnoses and interventions for thyroid disorders in diabetic patients, potentially improving their quality of life and overall healthcare outcomes. | |
| 653 | |a Datasets | ||
| 653 | |a Hormones | ||
| 653 | |a Preprocessing | ||
| 653 | |a Support vector machines | ||
| 653 | |a Health care | ||
| 653 | |a Disorders | ||
| 653 | |a Chronic illnesses | ||
| 653 | |a Algorithms | ||
| 653 | |a Balancing | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Machine learning | ||
| 653 | |a Decision trees | ||
| 700 | 1 | |a Mahmood, Salma A |u Department of Intelligent Medical Systems, University of Basrah, College of Computer Sciences and Information Technology, Basrah, Iraq | |
| 700 | 1 | |a Hamadi, Saad S |u Department of Internal Medicine, University of Basrah, College of Medicine, Basrah, Iraq | |
| 773 | 0 | |t Informatica |g vol. 49, no. 12 (Feb 2025), p. 105 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3188467301/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3188467301/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3188467301/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |