北京生物醫(yī)學(xué)工程

基于CatBoost算法和模糊分類系統(tǒng)的青年人血壓預(yù)測(cè)方法

Blood pressure prediction method based on CatBoost algorithm and fuzzy classification system in young people

作者：劉娟趙歡歡馬祖長(zhǎng) 王世軍

單位：中國(guó)電子科技集團(tuán)公司第三十八研究所（合肥230026）中國(guó)科學(xué)院合肥物質(zhì)科學(xué)研究院（合肥230026）中國(guó)科學(xué)技術(shù)大學(xué)科學(xué)島分院（合肥230026）滁州學(xué)院（安徽滁州239000）大連醫(yī)科大學(xué)（遼寧大連 116044）

關(guān)鍵詞：收縮壓; 舒張壓; CatBoost; 模糊分類系統(tǒng); 生活方式

分類號(hào)：R318

出版年·卷·期（頁(yè)碼）：2020·39·6（601-608）

摘要：

目的探究CatBoost算法在青年人血壓預(yù)測(cè)中的應(yīng)用價(jià)值，為青年人高血壓及高血壓前期預(yù)警提供一種可行的技術(shù)手段。方法以2015-2017年期間在北京某醫(yī)院體檢中心進(jìn)行健康體檢的3872位青年人為研究對(duì)象，基于人口統(tǒng)計(jì)學(xué)和生活方式等指標(biāo),分別利用CatBoost算法構(gòu)建收縮壓預(yù)測(cè)模型和舒張壓預(yù)測(cè)模型，然后利用模糊分類系統(tǒng)預(yù)測(cè)血壓分級(jí)。使用線性回歸、人工神經(jīng)網(wǎng)絡(luò)和SVM三種機(jī)器學(xué)習(xí)算法分別構(gòu)建血壓預(yù)測(cè)模型，并與CatBoost模型進(jìn)行比較分析。以均方根誤差（root mean square error，RMSE）和平均絕對(duì)百分比誤差（mean absolute percentage error, MAPE）作為模型的評(píng)價(jià)指標(biāo)。并進(jìn)一步的分析模糊分類系統(tǒng)的預(yù)測(cè)效果。結(jié)果對(duì)于收縮壓預(yù)測(cè)，基于CatBoost的模型在測(cè)試集上表現(xiàn)最優(yōu)，RMSE和MAPE分別為11.17和7. 18%，對(duì)于舒張壓預(yù)測(cè)，基于CatBoost的模型在測(cè)試集上表現(xiàn)最優(yōu)，RMSE和MAPE分別為9.04和9.29%。進(jìn)一步的模糊分類也取得了較好的血壓分類準(zhǔn)確性。變量重要性分析表明影響青年人血壓值最重要的四個(gè)因素依次是年齡、身體質(zhì)量指數(shù)(body mass index, BMI)、家族史和腰高比(waist-to-height ratio, WHtR)。結(jié)論 CatBoost算法在青年人血壓預(yù)測(cè)中的應(yīng)用具有一定的可行性，相比于其他傳統(tǒng)算法，具有更好的預(yù)測(cè)能力。結(jié)合模糊分類系統(tǒng)，可以給用戶較準(zhǔn)確的血壓分級(jí)預(yù)測(cè)。

Objective To explore the application value of CatBoost algorithm in blood pressure prediction for young people and provide a feasible technical means for early warning of hypertension and prehypertension in young people. Methods A total of 3872 young people who underwent a health check-up in a physical examination center in a Beijing hospital from 2015 to 2017 were selected as research objects, based on demography and lifestyle indicators, the CatBoost algorithm was used to construct a systolic blood pressure (SBP) prediction model and a diastolic blood pressure (DBP) prediction model respectively, and then fuzzy classification system was employed to predict blood pressure classification. In addition, blood pressure prediction models were constructed respectively using three machine learning algorithms, linear regression, artificial neural network and SVM, and compared with the CatBoost models. Root mean square error (RMSE) and mean absolute percentage error (MAPE) were used as the evaluation indicators of the models. The prediction effect of the fuzzy classification system was further analyzed. Results For SBP prediction, the CatBoost model performed the best on the test set, RMSE and MAPE were 11.17 and 7.18% respectively. For diastolic blood pressure prediction, the CatBoost model also performed the best on the test set, RMSE and MAPE were 9.04 and 9.29% respectively. Further fuzzy classification had also achieved good accuracy. The analysis of variable importance analysis showed that the four most important factors affecting blood pressure in young people were age, Body Mass Index(BMI), family history, and waist-to-height ratio(WHtR). Conclusions The CatBoost algorithm is feasible in the prediction of blood pressure in young people. Compared with other traditional algorithms, it has better predictive capability. Combined with fuzzy classification system, it can give users accurate prediction of blood pressure classification.

參考文獻(xiàn)：

[1] Wu JH, Wei W, Zhang L, et al. Risk assessment of hypertension in steel workers based on LVQ and Fisher-SVM deep excavation[J]. IEEE Access, 2019, 7: 23109-23119.
[2] Ansah JP, Inn RLH, Ahmad S. An evaluation of the impact of aggressive hypertension, diabetes and smoking cessation management on CVD outcomes at the population level: a dynamic simulation analysis[J]. BMC Public Health, 2019, 19(1): 1105.
[3] Fernández-Ruiz I. Systolic and diastolic hypertension independently predict CVD risk[J]. Nature Reviews Cardiology, 2019,16(10): 578-579.
[4] Wang ZW, Chen Z, Zhang LF, et al. Status of hypertension in China results from the China Hypertension Survey, 2012-2015 [J]. Circulation, 2018, 137(22): 2344-2356.
[5] Ghosh S, Banerjee A, Ray N, et al. Continuous blood pressure prediction from pulse transit time using ECG and PPG signals[C]//IEEE Healthcare Innovation Point-of-Care Technologies Conference. Cancun, Mexico: IEEE Press, 2016.
[6] Munnoch R, Jiang P. A personal medical device for multi-sensor, remote vital signs collection in the elderly[C]// 2015 Science and Information Conference. London, UK: IEEE Press, 2015:1122-1131.
[7] Sideris C, Kalantarian H, Nemati E, et al. Building Continuous Arterial Blood Pressure Prediction Models Using Recurrent Networks[C]//2016 IEEE International Conference on Smart Computing (SMARTCOMP). St Louis, MO, USA: IEEE, 2016: 1-5.
[8] Simjanoska M, Kochev S, Tanevski J, et al. Multi-level information fusion for learning a blood pressure predictive model using sensor data [J]. Information Fusion, 2020, 58: 24-39.
[9] Kwong EW-Y, Wu H, Pang GK-H. A prediction model of blood pressure for telemedicine [J]. Health Informatics Journal, 2016，24（3）: 227-244.
[10] 徐繼偉, 楊云. 集成學(xué)習(xí)方法:研究綜述 [J]. 云南大學(xué)學(xué)報(bào):自然科學(xué)版, 2018, 40(6): 36-46.
Xu JW, Yang Y. A survey of ensemble learning approaches[J]. Journal of Yunnan University（Natural Science）, 2018, 40(6): 36-46.
[11] 苗豐順, 李巖, 高岑, 等. 基于CatBoost算法的糖尿病預(yù)測(cè)方法[J]. 計(jì)算機(jī)系統(tǒng)應(yīng)用, 2019, 28(9): 215-218.
Miao FS, Li Y, Gao C, et al. Diabetes prediction method based on CatBoost algorithm[J]. Computer Systems & Applications, 2019, 28(9): 215-218.
[12] 王斌, 馮慧芬, 王芳, 等. 基于機(jī)器學(xué)習(xí)的Cat Boost模型在預(yù)測(cè)重癥手足口病中的應(yīng)用 [J]. 中國(guó)感染控制雜志, 2019, 18(1): 12-16.
Wang B, Feng HF, Wang F, et al. Application of CatBoost model based on machine learning in predicting severe hand- foot-mouth disease[J]. Chinese Journal of Infection Control, 2019, 18(1): 12-16.
[13] 《中國(guó)高血壓防治指南》修訂委員會(huì). 中國(guó)高血壓防治指南2018年修訂版 [J]. 心腦血管病防治, 2019, 19(1): 1-44.
[14] 張冬冬, 劉雪嬌, 王炳源, 等. 我國(guó)成年人群年齡對(duì)體質(zhì)指數(shù)與高血壓發(fā)病風(fēng)險(xiǎn)的修飾效應(yīng)[J]. 中華流行病學(xué)雜志, 2018, 39(6): 765-769.
Zhang DD, Liu XJ, Wang BY, et al. Age-related modification effect on the association between body mass index and the risk of hypertension: A Cohort Study on Chinese people living in the rural areas [J]. Chinese Journal of Epidemiology, 2018, 39(6): 765-769.
[15] 李婧雯, 張曉卉, 尹新華. 肥胖相關(guān)高血壓的研究進(jìn)展[J]. 臨床與病理雜志, 2020, 40(4): 1006-1011.
Li JW, Zhang XH, Yin XH. Progress in obesity-related hypertension[J]. Journal of Clinical and Pathological Research, 2020, 40(4): 1006-1011.
[16] Zhang M, Zhao Y, Wang G, et al. Body mass index and waist circumference combined predicts obesity-related hypertension better than either alone in a rural Chinese population [J]. Scientific Reports, 2016, 6： 31935.
[17] Huang XB, Chen F, Dai W, et al. Prevalence and risk factors associated with hypertension in the Chinese Qiang population[J]. Clinical and Experimental Hypertension, 2018, 40(5): 427-433.
[18] 張艷艷, 何朝, 趙瑩穎, 等. 北京市順義區(qū)人群中心型肥胖與心血管疾病危險(xiǎn)因素分析[J]. 中國(guó)健康教育, 2016 , 32( 5) : 419-423.
Zhang YY, He C, Zhao YY, et al. Relationship between central obesity and cardiovascular risk factors among residents of Shunyi District, Beijing[J]. Chinese Journal of Health Education, 2016 , 32( 5) : 419-423.
[19] 蘇健,向全永,呂淑榮,等.江蘇省成人中心型肥胖與心血管疾病危險(xiǎn)因素及其聚集性的關(guān)系[J].中華心血管病雜志,2015,43( 6) : 548-553.
Su J, Xiang QY, Lyu SR, et al. Relationship between central obesity and clustering of cardiovascular risk factors in adults of Jiangsu Province [J] . Chinese Journal of Cardiology, 2015,43( 6) : 548-553.
[20] 沈振海, 陸昀, 祁華金,等. 理想心血管健康與腰圍、腰高比的相關(guān)性研究[J]. 中華健康管理學(xué)雜志, 2017, 11(1): 56-61.
Shen ZH, Lu Y, Qi HJ, et al. Association between ideal cardiovascular health and waist circumference, waist-to-height ratio [J]. Chinese Journal of Health Management, 2017, 11(1): 56-61.
[21] 汪宏莉, 韓延柏, 陳濤, 等. 老年人高血壓風(fēng)險(xiǎn)預(yù)測(cè)肥胖指標(biāo)篩選[J]. 中國(guó)公共衛(wèi)生, 2020, 36(1): 105-109.
Wang HL, Han YB, Chen T, et al. Screening on obesity indexes for predicting hypertension risk in 60 – 69 years old community residents[J]. Chinese Journal of Public Health, 2020, 36(1): 105-109.

服務(wù)與反饋：

【文章下載】【加入收藏】

提示：您還未登錄，請(qǐng)登錄！點(diǎn)此登錄

51黑料吃瓜在线观看,51黑料官网|51黑料捷克街头搭讪_51黑料入口最新视频