北京生物醫(yī)學(xué)工程

基于多粒度級(jí)聯(lián)森林的骨質(zhì)疏松性骨折預(yù)測(cè)研究

Prediction of osteoporotic fracture based on multi-grained cascade forest

作者：徐輝煌張海宇林勇

單位：上海理工大學(xué)醫(yī)療器械與食品學(xué)院(上海 200093)

關(guān)鍵詞：機(jī)器學(xué)習(xí)；骨質(zhì)疏松性骨折； t分布鄰域嵌入；隨機(jī)森林；多粒度級(jí)聯(lián)森林

分類號(hào)：R318；TP181

出版年·卷·期（頁(yè)碼）：2019·38·4（384-391）

摘要：

目的骨質(zhì)疏松性骨折(osteoporotic fracture, OF)的預(yù)測(cè)對(duì)于骨折防范具有重要的臨床指導(dǎo)意義。針對(duì)傳統(tǒng)logistic回歸預(yù)測(cè)模型存在的精度不高和未考慮遺傳因子問(wèn)題，本文引入多粒度級(jí)聯(lián)森林(multi-grained cascade forest, gcForest)并結(jié)合遺傳因子來(lái)預(yù)測(cè)OF。方法首先基于t分布鄰域嵌入(t-distributed stochastic neighbor embedding, t-SNE)算法對(duì)OF關(guān)聯(lián)基因位點(diǎn)進(jìn)行非線性降維，降維后的基因位點(diǎn)與臨床因素構(gòu)成特征組。然后構(gòu)建gcForest模型對(duì)OF進(jìn)行預(yù)測(cè)。最后通過(guò)10次十折分層交叉驗(yàn)證與logistic、梯度提升決策樹(shù)、隨機(jī)森林進(jìn)行對(duì)比。結(jié)果基于gcForest的模型分類精度為0.892 7，AUC值為0.92±0.05，泛化性能最優(yōu)。結(jié)論在考慮遺傳因素的條件下，gcForest分類效果優(yōu)于其他模型，驗(yàn)證了本文方法的高效性和實(shí)用性。

Objective The prediction of osteoporotic fracture (OF) has important clinical guiding significance for fracture prevention. In view of the low precision of traditional logistic regression model and the lack of consideration of genetic factors, this paper introduces multi-grained cascade forest (gcForest) and combines genetic factors to predict the OF. Methods Firstly, based on the t-distributed stochastic neighbor embedding (t-SNE) method, the nonlinear descending dimension of the associated gene loci was carried out, and the gene loci and clinical factors were formed in the feature group after the reduction of the dimension. The gcForest model was then constructed to predict the OF. Finally, 10 times 10-fold stratified cross-validation was compared with logistic, gradient boosting decision tree and random forest. Results The model classification accuracy based on gcForest was 0.8927, AUC value was 0.92±0.05, and the generalization performance was optimal. Conclusions Under the condition of considering genetic factors, the gcForest classification effect is better than other models, which verifies the efficiency and practicability of the method.

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