Prediction of amputation risk of patients with diabetic foot using classification algorithms: A clinical study from a tertiary center.
Diabetic foot ulcers can have vital consequences, such as amputation for patients. The primary purpose of this study is to predict the amputation risk of diabetic foot patients using machine‐learning classification algorithms. In this research, 407 patients treated with the diagnosis of diabetic foo...
| Publicado en: | International Wound Journal Vol. 21; no. 1; pp. 1 - 16 |
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| Autores principales: | , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jan2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175054766&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175054766 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17424801 1725 jtl: International Wound Journal issn: 17424801 maglogo: Y pubinfo: dt: Jan2024 vid: 21 iid: 1 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 175054766 175054766 175054766 10.1111/iwj.14556 175054766 ppf: 1 ppct: 15 formats: tig: atl: Prediction of amputation risk of patients with diabetic foot using classification algorithms: A clinical study from a tertiary center. aug: au: Demirkol, Denizhan Erol, Çiğdem Selçukcan Tannier, Xavier Özcan, Tuncay Aktaş, Şamil affil: Faculty of Engineering, Department of Computer Engineering, Aydın Adnan Menderes University, Aydın, Turkey sug: subj: Diabetic Foot Classification Amputation Evaluation Risk Assessment Prediction Models Classification Algorithms Human Tertiary Health Care ab: Diabetic foot ulcers can have vital consequences, such as amputation for patients. The primary purpose of this study is to predict the amputation risk of diabetic foot patients using machine‐learning classification algorithms. In this research, 407 patients treated with the diagnosis of diabetic foot between January 2009–September 2019 in Istanbul University Faculty of Medicine in the Department of Undersea and Hyperbaric Medicine were retrospectively evaluated. Principal Component Analysis (PCA) was used to identify the key features associated with the amputation risk in diabetic foot patients within the dataset. Thus, various prediction/classification models were created to predict the "overall" risk of diabetic foot patients. Predictive machine‐learning models were created using various algorithms. Additionally to optimize the hyperparameters of the Random Forest Algorithm (RF), experimental use of Bayesian Optimization (BO) has been employed. The sub‐dimension data set comprising categorical and numerical values was subjected to a feature selection procedure. Among all the algorithms tested under the defined experimental conditions, the BO‐optimized "RF" based on the hybrid approach (PCA‐RF‐BO) and "Logistic Regression" algorithms demonstrated superior performance with 85% and 90% test accuracies, respectively. In conclusion, our findings would serve as an essential benchmark, offering valuable guidance in reducing such hazards. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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