Machine learning‐based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy
ABSTRACT Background Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes, marked by symptoms like hyperalgesia, numbness, and swelling that impair quality of life. Nerve conduction abnormalities in DPN significantly increase the risk of neuropathic foot ulcers (NFU), whi...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Journal of Diabetes Investigation |
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| Online Access: | https://doi.org/10.1111/jdi.70010 |
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| author | Ge Shi Zhenxuan Gao Ze Zhang Quanyu Jin Sitong Li Jiaxin Liu Lei Kou Abudurezhake Aerman Wenqiang Yang Qi Wang Furong Cai Li Zhang |
| author_facet | Ge Shi Zhenxuan Gao Ze Zhang Quanyu Jin Sitong Li Jiaxin Liu Lei Kou Abudurezhake Aerman Wenqiang Yang Qi Wang Furong Cai Li Zhang |
| author_sort | Ge Shi |
| collection | DOAJ |
| description | ABSTRACT Background Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes, marked by symptoms like hyperalgesia, numbness, and swelling that impair quality of life. Nerve conduction abnormalities in DPN significantly increase the risk of neuropathic foot ulcers (NFU), which can progress rapidly and lead to severe outcomes, including infection, gangrene, and amputation. Early prediction of NFU in DPN patients is crucial for timely intervention. Methods Clinical data from 400 DPN patients treated at the China–Japan Friendship Hospital (September 2022–2024) were retrospectively analyzed. Data included medical histories, physical exams, biochemical tests, and imaging. After feature selection and data balancing, the dataset was split into training and validation subsets (8:2 ratio). Six machine learning algorithms—random forest, decision tree, logistic regression, K‐nearest neighbor, extreme gradient boosting, and multilayer perceptron—were evaluated using k‐fold cross‐validation. Model performance was assessed via accuracy, precision, recall, F1 score, and AUC. The SHAP method was employed for interpretability. Results The multilayer perceptron model showed the best performance (accuracy: 0.875; AUC: 0.901). SHAP analysis highlighted triglycerides, high‐density lipoprotein cholesterol, diabetes duration, age, and fasting blood glucose as key predictors. Conclusions A machine learning‐based prediction model using a multilayer perceptron algorithm effectively identifies DPN patients at high NFU risk, offering clinicians an accurate tool for early intervention. |
| format | Article |
| id | doaj-art-13a94e58e1764e749f536696ec051bf4 |
| institution | OA Journals |
| issn | 2040-1116 2040-1124 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Investigation |
| spelling | doaj-art-13a94e58e1764e749f536696ec051bf42025-08-20T02:05:20ZengWileyJournal of Diabetes Investigation2040-11162040-11242025-06-011661055106410.1111/jdi.70010Machine learning‐based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathyGe Shi0Zhenxuan Gao1Ze Zhang2Quanyu Jin3Sitong Li4Jiaxin Liu5Lei Kou6Abudurezhake Aerman7Wenqiang Yang8Qi Wang9Furong Cai10Li Zhang11China–Japan Friendship School of Clinical Medicine Capital Medical University Beijing ChinaGraduate School of Peking Union Medical College Chinese Academy of Medical Sciences and Peking Union Medical College Beijing ChinaChina–Japan Friendship School of Clinical Medicine Peking University Beijing ChinaGraduate School of Peking Union Medical College Chinese Academy of Medical Sciences and Peking Union Medical College Beijing ChinaInstitute of Clinical Medical Sciences China–Japan Friendship Hospital Beijing ChinaInstitute of Clinical Medical Sciences China–Japan Friendship Hospital Beijing ChinaChina–Japan Friendship School of Clinical Medicine Peking University Beijing ChinaChina–Japan Friendship School of Clinical Medicine Peking University Beijing ChinaThe Department of Neurosurgery China–Japan Friendship Hospital Beijing ChinaThe Department of Neurosurgery China–Japan Friendship Hospital Beijing ChinaChang Chun Institute of Applied Chemistry Chinese Academy of Sciences Changchun City Jilin Province ChinaThe Department of Neurosurgery China–Japan Friendship Hospital Beijing ChinaABSTRACT Background Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes, marked by symptoms like hyperalgesia, numbness, and swelling that impair quality of life. Nerve conduction abnormalities in DPN significantly increase the risk of neuropathic foot ulcers (NFU), which can progress rapidly and lead to severe outcomes, including infection, gangrene, and amputation. Early prediction of NFU in DPN patients is crucial for timely intervention. Methods Clinical data from 400 DPN patients treated at the China–Japan Friendship Hospital (September 2022–2024) were retrospectively analyzed. Data included medical histories, physical exams, biochemical tests, and imaging. After feature selection and data balancing, the dataset was split into training and validation subsets (8:2 ratio). Six machine learning algorithms—random forest, decision tree, logistic regression, K‐nearest neighbor, extreme gradient boosting, and multilayer perceptron—were evaluated using k‐fold cross‐validation. Model performance was assessed via accuracy, precision, recall, F1 score, and AUC. The SHAP method was employed for interpretability. Results The multilayer perceptron model showed the best performance (accuracy: 0.875; AUC: 0.901). SHAP analysis highlighted triglycerides, high‐density lipoprotein cholesterol, diabetes duration, age, and fasting blood glucose as key predictors. Conclusions A machine learning‐based prediction model using a multilayer perceptron algorithm effectively identifies DPN patients at high NFU risk, offering clinicians an accurate tool for early intervention.https://doi.org/10.1111/jdi.70010Diabetic peripheral neuropathyMachine learningPrediction model |
| spellingShingle | Ge Shi Zhenxuan Gao Ze Zhang Quanyu Jin Sitong Li Jiaxin Liu Lei Kou Abudurezhake Aerman Wenqiang Yang Qi Wang Furong Cai Li Zhang Machine learning‐based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy Journal of Diabetes Investigation Diabetic peripheral neuropathy Machine learning Prediction model |
| title | Machine learning‐based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy |
| title_full | Machine learning‐based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy |
| title_fullStr | Machine learning‐based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy |
| title_full_unstemmed | Machine learning‐based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy |
| title_short | Machine learning‐based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy |
| title_sort | machine learning based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy |
| topic | Diabetic peripheral neuropathy Machine learning Prediction model |
| url | https://doi.org/10.1111/jdi.70010 |
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