Establishment of a prediction model of risk factors for psoriasis vulgaris based on machine learning

XU Ruiyao; YAN Xiaoning; HUANG Yongmei; JING Huiling; ZHAO Chan; WANG Yasha; SUN Liping; ZHAO Yiding

doi:10.16766/j.cnki.issn.1674-4152.003705

Volume 22 Issue 10

Oct. 2024

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > Chinese Journal of General Practice > 2024 > 22(10): 1656-1659

XU Ruiyao, YAN Xiaoning, HUANG Yongmei, JING Huiling, ZHAO Chan, WANG Yasha, SUN Liping, ZHAO Yiding. Establishment of a prediction model of risk factors for psoriasis vulgaris based on machine learning[J]. Chinese Journal of General Practice, 2024, 22(10): 1656-1659. doi: 10.16766/j.cnki.issn.1674-4152.003705

Citation:

XU Ruiyao, YAN Xiaoning, HUANG Yongmei, JING Huiling, ZHAO Chan, WANG Yasha, SUN Liping, ZHAO Yiding. Establishment of a prediction model of risk factors for psoriasis vulgaris based on machine learning[J]. Chinese Journal of General Practice, 2024, 22(10): 1656-1659. doi: 10.16766/j.cnki.issn.1674-4152.003705

Citation:

XU Ruiyao, YAN Xiaoning, HUANG Yongmei, JING Huiling, ZHAO Chan, WANG Yasha, SUN Liping, ZHAO Yiding. Establishment of a prediction model of risk factors for psoriasis vulgaris based on machine learning[J]. Chinese Journal of General Practice, 2024, 22(10): 1656-1659. doi: 10.16766/j.cnki.issn.1674-4152.003705

PDF( 517 KB)

Establishment of a prediction model of risk factors for psoriasis vulgaris based on machine learning

doi: 10.16766/j.cnki.issn.1674-4152.003705

XU Ruiyao^{1, 2}
, YAN Xiaoning¹
, HUANG Yongmei³
, JING Huiling⁴
, ZHAO Chan⁵
, WANG Yasha⁶
, SUN Liping⁷
, ZHAO Yiding^{1
,
,}

1.
Department of Dermatology, Shaanxi Traditional Chinese Medicine Hospital, Xi'an, Shaanxi 710003, China

Funds:

SZY-KJCYC-2023-005

2021ZDLSF04-12

2019ZDLSF04-08

Received Date: 2024-02-28
Available Online: 2024-12-28

Abstract

Abstract

Objective This study aims to identify sensitive indicators and their combinations to distinguish psoriasis, providing support for augmenting diagnostic evaluations and clinical decisions. Methods A total of 301 patients with psoriasis vulgaris from multiple centers, who visited from November 1, 2019 to October 31, 2021, were included. Data were collected on general characteristics, psoriasis scale information, TCM syndrome, and laboratory tests. Six machine learning algorithms were used to construct and verify the classification model for predicting mild and severe psoriasis. The classification performance of the models was evaluated by the parameters of receiver operating characteristic (ROC) area under the curve (AUC), precision, recall, and accuracy. Result The Naive Bayesian Model achieve an AUC of 0.801 4, while the AUC values of the other implicit classification models of logistic regression, SVM, random forest, AdaBoost, and SGD were 0.759 1, 0.754 6, 0.800 4, 0.712 6, and 0.773 7, respectively. Both Bayesian and random forest models had true positive rates of more than 80% in the ROC curve. Conclusion "DLQI", "TCM syndrome", "Baso", "PDW", and "Onset season" were found as key indicators for predicting psoriasis severity. Machine learning and data mining can use real-world clinical data to develop predictive models for psoriasis risk factors, providing a basis for early disease assessment and medical decision-making.
- Psoriasis vulgaris,
- Machine learning,
- Severity,
- Risk factors,
- Prediction model

FullText(HTML)

References(18)

References

[1]	中华医学会皮肤性病学分会银屑病专业委员会, 中国银屑病诊疗指南(2023版)[J]. 中华皮肤科杂志, 2023, 56(7): 573-625. Psoriasis Professional Committee of Chinese Society of Dermatology and Venereology, Guidelines for the Diagnosis and Treatment of Psoriasis in China (2023 Edition)[J]. Chinese Journal of Dermatology, 2023, 56(7): 573-625.
[2]	史玉玲. 《中国银屑病诊疗指南(2023版)》解读[J]. 同济大学学报(医学版), 2023, 44(5): 631-633. SHI Y L. Interpretation of the Guidelines for the Diagnosis and Treatment of Psoriasis in China (2023 Edition)[J]. Journal of Tongji University (Medical Science), 2023, 44(5): 631-633.
[3]	ROWE M. An introduction to machine learning for clinicians[J]. Acad Med, 2019, 94(10): 1433-1436. doi: 10.1097/ACM.0000000000002792
[4]	TUNTHANATHIP T, SAE-HENG S, OEARSAKUL T, et al. Machine learning applications for the prediction of surgical site infection in neurological operations[J]. Neurosurgical focus, 2019, 47(2): E7. DOI: 10.3171/2019.5.FOCUS19241.
[5]	ADLER E D, VOORS A A, KLEIN L, et al. Improving risk prediction in heart failure using machine learning[J]. Eur J Heart Fail, 2020, 22(1): 139-147. doi: 10.1002/ejhf.1628
[6]	赵辨. 中国临床皮肤病学[M]. 2版. 南京: 江苏凤凰科学技术出版社, 2017: 1104-1126. ZHAO B. Chinese Journal of Clinical Dermatology[M]. 2 Edition. Nanjing: Jiangsu Phoenix Science and Technology Press, 2017: 1104-1126.
[7]	HERATER F, BITTNER Z, CODREA M C, et al. Platelets aggregate with neutrophils and promote skin pathology in psoriasis[J]. Front Immunol, 2019, 101867. DOI: 10.3389/fimmu.2019.01867.
[8]	徐黎明, 王力, 范志佳, 等. 血小板相关参数在银屑病中的临床价值探讨[J]. 检验医学, 2019, 34(4): 335-338. XU L M, WANG L, FAN Z J, et al. Clinical value of platelet-related parameters in psoriasis[J]. Laboratory Medicine, 2019, 34(4): 335-338.
[9]	ZHOU J, LI Y, GUO X. Predicting psoriasis using routine laboratory tests withrandom forest[J]. PLoS One, 2021, 16(10): e258768. DOI: 10.1371/journal.pone.0258768.
[10]	杨庆琪, 刘芳, 李园园. 不同分型分期银屑病患者外周血指标的初步分析[J]. 中国疗养医学, 2021, 30(6): 587-591. YANG Q Q, LIU F, LI Y Y. Preliminary analysis of peripheral blood indexes in patients with psoriasis at different types and stages[J]. Chinese Healing Medicine, 2021, 30(6): 587-591.
[11]	LIU Z, PERRY L A, MORGAN V. The association between platelet indices and presence and severity of psoriasis: a systematic review and meta-analysis[J]. Clin Exp Med, 2023, 23(2): 333-346.
[12]	SIRIN M C, KORKMAZ S, ERTURAN I, et al. Evaluation of monocyte to HDL cholesterol ratio and other inflammatory markers in patients with psoriasis[J]. An Bras Dermatol, 2020, 95(5): 575-582. doi: 10.1016/j.abd.2020.02.008
[13]	HASHIMOTO T, ROSEN J D, SANDERS K M, et al. Possible roles of basophils in chronic itch[J]. Exp Dermatol, 2019, 28(12): 1373-1379.
[14]	殷文浩, 金梦祝, 戴晴. 火针配合针刺治疗小腿斑块状银屑病的效果观察[J]. 中华全科医学, 2022, 20(1): 117-120. doi: 10.16766/j.cnki.issn.1674-4152.002291 YIN W H, JIN M Z, DAI Q. Effect of fire acupuncture combined with acupuncture in the treatment of calf plaque psoriasis[J]. Chinese Journal of General Practice, 2022, 20(1): 117-120. doi: 10.16766/j.cnki.issn.1674-4152.002291
[15]	李爱君, 母淑娟, 姚瑶, 等. 中医对糖皮质激素药理作用理解的探讨[J]. 山西医药杂志, 2018, 47(6): 700-701. LI A J, MU S J, YAO Y, et al. Discussion on the understanding of the pharmacological effects of glucocorticoids in traditional Chinese medicine[J]. Shanxi Medical Journal, 2018, 47(6): 700-701.
[16]	高酥, 张家瑜, 陈易, 等. 链球菌感染与银屑病相关性探究[J]. 皮肤病与性病, 2021, 43(3): 340-341. GAO S, ZHANG J Y, CHEN Y, et al. Correlation between streptococcal infection and psoriasis[J]. Dermatology & STD, 2021, 43(3): 340-341.
[17]	GAMONAL S B L, GAMONAL A C C, MARQUES N C V, et al. Is vitamin D status relevant to psoriasis and psoriatic arthritis? A retrospective cross-sectional study[J]. Sao Paulo Med J, 2022, 141(3): e2022216. DOI: 10.1590/1516-3180.2022.0216.R1.01072022.
[18]	黄珊, 白彦萍. 基于中医理论探究银屑病的季节规律性[J]. 中华中医药杂志, 2021, 36(10): 6209-6211. HUANG S, BAI Y P. Exploring the Seasonal Regularity of Psoriasis Based on Traditional Chinese Medicine Theory[J]. Chinese Journal of Traditional Chinese Medicine, 2021, 36(10): 6209-6211.

Relative Articles

[1]	LI Qingfeng, LIU Huan, DONG Gaiqin. Risk factors for epileptic seizures after febrile seizures in children and construction of a nomogram prediction model[J]. Chinese Journal of General Practice, 2024, 22(1): 86-88. doi: 10.16766/j.cnki.issn.1674-4152.003337
[2]	YANG Jing, LIU Mei, YANG Lei, GU Zejuan. Risk factors and predictive model construction of postoperative delirium in elderly patients with hip fracture[J]. Chinese Journal of General Practice, 2024, 22(9): 1596-1600. doi: 10.16766/j.cnki.issn.1674-4152.003691
[3]	ZHAO Xin, LIU Qianqian, LI Hangyu. Study on cardiovascular disease risk prediction model for hemodialysis patients[J]. Chinese Journal of General Practice, 2024, 22(5): 742-745. doi: 10.16766/j.cnki.issn.1674-4152.003493
[4]	XIAO Zhimei, DING Chenhui, LUO Chen, QIAN Leilei. Risk factors of left ventricular hypertrophy for chronic kidney disease patients and construction of a predictive model[J]. Chinese Journal of General Practice, 2024, 22(8): 1354-1358. doi: 10.16766/j.cnki.issn.1674-4152.003635
[5]	HONG Jiahui, LIU Yongyang, FANG Yukun, QIU Yiyin, FANG Kai, CHEN Qingyong. CRP/ALB combined with lung consolidation in predicting the severity of COVID-19[J]. Chinese Journal of General Practice, 2024, 22(7): 1116-1120. doi: 10.16766/j.cnki.issn.1674-4152.003579
[6]	XU Zu-liang, SHENG Liang, HE Xia-xia, WANG Yan-na, WANG Guo-yu. Multi-sequence MRI image data-based machine learning model in the diagnosis of benign and malignant parotid gland tumours[J]. Chinese Journal of General Practice, 2023, 21(1): 108-111. doi: 10.16766/j.cnki.issn.1674-4152.002824
[7]	ZHANG Jun-jie, HAO Li-gang, XU Qian, FENG Hui, ZHANG Ning, SHI Gao-feng. CT-derived model for the diagnosis of pulmonary invasive mucinous adenocarcinoma by machine learning[J]. Chinese Journal of General Practice, 2023, 21(1): 6-9. doi: 10.16766/j.cnki.issn.1674-4152.002799
[8]	LU Wen-ting, YAO Yuan, XIONG Jing, LIU Xiang-ping, LI Shuang-qing. Effect of machine learning in the auxiliary diagnosis model of cardiovascular disease[J]. Chinese Journal of General Practice, 2023, 21(1): 112-117. doi: 10.16766/j.cnki.issn.1674-4152.002825
[9]	ZHAO Shiqi, WANG Shu, ZHANG Ying, WANG Shasha, LI Jing, TANG Wenjuan, LI Jing, ZHANG Xuemin, LI Fang. Establishment and validation of model for the risk of Carbapenem-Resistant Enterobacteriaceae infection in elderly patients with pulmonary infection[J]. Chinese Journal of General Practice, 2023, 21(11): 1860-1864. doi: 10.16766/j.cnki.issn.1674-4152.003243
[10]	MAO Xiao-qiang, JIN Jing, YU Guo-feng. Relationship between coagulation and fibrinolysis imbalance and severity of craniocerebral injury and its predictive value for acute traumatic coagulopathy[J]. Chinese Journal of General Practice, 2022, 20(3): 407-410. doi: 10.16766/j.cnki.issn.1674-4152.002364
[11]	LUO Jian, XU Zhao-jun. Relationship between plasma B-type natriuretic peptide and severity of craniocerebral injury and its predictive effect on cerebral heart syndrome[J]. Chinese Journal of General Practice, 2022, 20(2): 233-236. doi: 10.16766/j.cnki.issn.1674-4152.002320
[12]	JIN De-wei, TAO Min, MA Xiao-yan, WANG Yi, SHENG Li-li, LIU Na. Study on risk factors of N-terminal pro-brain natriuretic peptide abnormalities in non-dialysis patients with chronic kidney disease[J]. Chinese Journal of General Practice, 2021, 19(8): 1314-1317. doi: 10.16766/j.cnki.issn.1674-4152.002049
[13]	LIU Chu-chu, HUANG Jie, LIU Yu-hui, ZHOU Hua-dong, XU Li. Risk factors and recurrence characteristics of neuromyelitis optica spectrum disease[J]. Chinese Journal of General Practice, 2021, 19(5): 715-718,829. doi: 10.16766/j.cnki.issn.1674-4152.001899
[14]	WANG Xiao-li, SHI Tian-xing, PENC De-rong, WANG ZIhao-xin, WANC Hui, SHI Jian-wei, YU Wen-ya. Comparative study on the effectiveness of two machine learning algorithms in constructing risk assessment models of coronary heart disease in the elderly[J]. Chinese Journal of General Practice, 2021, 19(4): 523-527. doi: 10.16766/j.cnki.issn.1674-4152.001852
[15]	CHEN Gang, HUANG Ming-wei, KONG Lai-fa, SHI Tao, FANG Hao. Analysis of etiology and risk factors of wound infection after closed pelvic fracture[J]. Chinese Journal of General Practice, 2020, 18(7): 1128-1130. doi: 10.16766/j.cnki.issn.1674-4152.001446
[16]	XING Yi, JIANG Fang-fang, JIANG Bo, SHI Chun-sheng. Analysis of the epidemiology and risk factors of pterygium in 116 elderly patients in Hefei area[J]. Chinese Journal of General Practice, 2020, 18(8): 1279-1282. doi: 10.16766/j.cnki.issn.1674-4152.001485
[17]	NI Yang, MA Jia-li, WANG Su-ping, HU Peng, CHEN Zhong-jian. A study on the economic burden of psoriasis vulgaris inpatients with zero price difference policy[J]. Chinese Journal of General Practice, 2020, 18(8): 1388-1391. doi: 10.16766/j.cnki.issn.1674-4152.001514
[18]	YIN Wen-hao, JIN Meng-zhu, WU Wan-xin. Expression and significance of HPA and VEGF in skin lesions of psoriasis vulgaris at progressing stage[J]. Chinese Journal of General Practice, 2019, 17(11): 1841-1844. doi: 10.16766/j.cnki.issn.1674-4152.001070
[19]	WANG Huiqin, TANG Chengbo, TANG Hong, WU Weidong. Expression and significance of miR-21 and its target protein RASA1 in the skin of patients with psoriasis vulgaris[J]. Chinese Journal of General Practice, 2017, 15(7): 1141-1143,1180. doi: 10.16766/j.cnki.issn.1674-4152.2017.07.014
[20]	LIN Xia, SUN Cheng-chao, ZHANG Xiang-yan, TU Shao-song, HUANG Jin-wei. Epidemiology and risk factors of Pseudomonas aeruginosa infection in patients with severe thoracic trauma and mechanical ventilation[J]. Chinese Journal of General Practice, 2017, 15(3): 518-520. doi: 10.16766/j.cnki.issn.1674-4152.2017.03.047

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(3) / Tables(4)

Get Citation

PDF

XML

Article Metrics

Article views (116) PDF downloads(9)

Establishment of a prediction model of risk factors for psoriasis vulgaris based on machine learning

doi: 10.16766/j.cnki.issn.1674-4152.003705

Abstract

References

Relative Articles

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Establishment of a prediction model of risk factors for psoriasis vulgaris based on machine learning

doi: 10.16766/j.cnki.issn.1674-4152.003705

Abstract

References

Relative Articles

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Proportional views

Related

Export File

Citation

Format

Content