Prediction of Patient Visits for Skin Diseases through Enhanced Evolutionary Computation and Ensemble Learning.
Skin diseases are an important global public health issue, causing significant health and psychological burdens. Predicting dermatology outpatient visits is essential for optimizing hospital resources and improving diagnosis and treatment methods. Based on machine learning technology and ensemble le...
| Publicado en: | Journal of Medical Systems Vol. 49; no. 1; pp. 1 - 13 |
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| Autores principales: | , , , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
4/23/2025
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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=184705968&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184705968 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 4/23/2025 vid: 49 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184705968 184705968 184705968 10.1007/s10916-025-02185-0 184705968 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of Patient Visits for Skin Diseases through Enhanced Evolutionary Computation and Ensemble Learning. aug: au: Leng, Wenting Yang, Chenglin Kou, Menggang Zhang, Kequan Liu, Xinyue affil: https://ror.org/01mkqqe32 The Second Hospital & Clinical Medical School, Lanzhou University, 730000, Lanzhou, China sug: subj: Algorithms Machine Learning Methods Ensemble Learning Methods Skin Diseases Diagnosis Skin Diseases Therapy Prediction Models Office Visits Evaluation Human Male Female Acne Vulgaris Therapy Outpatients Health Resource Allocation Quality Improvement Quality of Health Care Neural Networks (Computer) Time Series China Case Studies Models, Statistical Funding Source Sensitivity and Specificity Data Mining Particle Swarm Optimization Male Female ab: Skin diseases are an important global public health issue, causing significant health and psychological burdens. Predicting dermatology outpatient visits is essential for optimizing hospital resources and improving diagnosis and treatment methods. Based on machine learning technology and ensemble learning theory, this study integrates four neural network models to construct an optimal prediction model for daily outpatient visits related to skin diseases. To address the issue of local optima entrapment in sand cat swarm optimization (SCSO), an enhanced SCSO is proposed by incorporating the chaotic mapping, the spiral search strategy, and the sparrow warning mechanism. The enhanced SCSO is then utilized to optimize two critical parameters of variational mode decomposition, enabling the extraction of periodic patterns from the skin disease time series. Finally, the enhanced SCSO is applied again to determine the optimal weights for the ensemble model, thereby achieving optimal fusion predictions. We utilized ten years of outpatient data from the dermatology department of a hospital in China, and selected acne, the most prevalent skin condition in the region, as a case study. Experimental results demonstrate that the proposed model effectively combines the strengths of each module, achieving an root mean squared error (RMSE) of 4.43 and an R-squared (R2) of 0.98. Compared to individual models, the RMSE and R2 are improved by 79.69% and 36.97%, respectively, effectively overcoming the limitations of single-model approaches. This research provides valuable insights for leveraging medical time series data and optimizing healthcare resource allocation. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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