北京生物醫(yī)學(xué)工程

基于CatBoost算法的中青年頸動脈粥樣硬化預(yù)測方法

Carotid arteriosclerosis prediction method based on CatBoost algorithm in young and middle ages

作者：丁瑤張小玉許楊高理升孫怡寧王世軍馬祖長

單位：中國科學(xué)院合肥智能機械研究所（合肥 230031）;中國科學(xué)技術(shù)大學(xué)（合肥230026）;大連醫(yī)科大學(xué)（遼寧大連 116044）

關(guān)鍵詞：頸動脈粥樣硬化；特征選擇；CatBoost；Logistic回歸；人工神經(jīng)網(wǎng)絡(luò)

分類號：R318.04

出版年·卷·期（頁碼）：2020·39·5（470-476）

摘要：

目的探究CatBoost算法在中青年頸動脈粥樣硬化預(yù)測中的應(yīng)用價值，為中青年頸動脈粥樣硬化早期篩查提供一種可行的技術(shù)手段。方法以2016 -2018年期間在北京某醫(yī)院體檢中心進行健康體檢的2258位中青年為研究對象，根據(jù)頸動脈彩超檢查結(jié)果診斷是否有頸動脈粥樣硬化。使用下采樣技術(shù)對樣本進行平衡處理。分析變量重要性進行特征選擇，構(gòu)建CatBoost模型。利用Logistic回歸和人工神經(jīng)網(wǎng)絡(luò)兩類機器學(xué)習(xí)算法構(gòu)建模型，并與CatBoost模型進行比較分析。以靈敏度、特異性、準(zhǔn)確率及工作特征（receiver operating characteristic，ROC）曲線下的面積（area under the ROC curve, AUC）作為模型的評價指標(biāo)。結(jié)果 CatBoost模型在測試集上的靈敏度、特異性、準(zhǔn)確率和AUC均最高，分別為82.8%，96.7%，90.3%，0.92。Logistic回歸模型和神經(jīng)網(wǎng)絡(luò)模型的靈敏度、特異性和準(zhǔn)確率均介于62.4%~73.3%之間，AUC均介于0.72~0.78之間。重要性分析表明影響中青年頸動脈粥樣硬化最重要的三個因素依次是年齡、腰高比、高密度脂蛋白膽固醇。結(jié)論 CatBoost算法在中青年頸動脈粥樣硬化預(yù)測中的應(yīng)用具有一定的可行性。相比于其他傳統(tǒng)算法，具有較高的診斷價值。

Objective To explore the application value of CatBoost algorithm in the prediction of carotid atherosclerosis in young and middle-aged people and provide a feasible technical means for early screening of carotid arteriosclerosis in young and middle-aged people. Methods A total of 2258 young and middle-aged people who underwent a health checkup at a medical checkup center in a Beijing hospital from 2016 to 2018 were selected as the research subjects, carotid arteriosclerosis was diagnosed based on the results of carotid color doppler ultrasound. Samples were balanced using under-sampling techniques. Feature selection is performed by analyzing the importance of variables. The CatBoost prediction model was built. In addition, models were constructed using two types of machine learning algorithms, Logistic regression and artificial neural network, and compared with CatBoost model. Sensitivity, specificity, accuracy, and the ROC curve areas (AUC) were used as the evaluation indicators of the model. Results The CatBoost model had the highest sensitivity, specificity, accuracy, and AUC on the test set, which were 82.8%, 96.7%, 90.3%, and 0.92 respectively. The sensitivity, specificity and accuracy of the models constructed by Logistic regression and neural network were between 62.4% and 73.3%, and the AUCs were between 0.72 and 0.78.Importance analysis showed that the three most important factors affecting carotid arteriosclerosis in young and middle-aged people were age, waist-to-height ratio, and high-density lipoprotein cholesterol. Conclusions The CatBoost algorithm is feasible in the prediction of carotid sclerosis in young and middle-aged people. Compared with other traditional algorithms, it has higher diagnostic value.

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