基于多標(biāo)簽體檢數(shù)據(jù)的疾病風(fēng)險(xiǎn)分析方法研究
發(fā)布時(shí)間：2019-03-15 21:01
【摘要】：健康體檢是疾病預(yù)防很重要的環(huán)節(jié)。醫(yī)生可以根據(jù)個(gè)人的健康體檢結(jié)果及時(shí)分析潛在的病癥,進(jìn)而對(duì)其進(jìn)行健康指導(dǎo)。針對(duì)健康體檢結(jié)果的分析,傳統(tǒng)的處理方式為有經(jīng)驗(yàn)的醫(yī)生針對(duì)身體各部分的體檢結(jié)果給出整體的健康狀況和疾病風(fēng)險(xiǎn)分析,隨著數(shù)據(jù)的日益增多,以及醫(yī)生經(jīng)驗(yàn)的良莠不齊等現(xiàn)狀,人工的分析方法在效率和精度方面無(wú)法滿足日益增多的體檢需求。隨著數(shù)據(jù)挖掘技術(shù)的發(fā)展,人工智能、機(jī)器學(xué)習(xí)方法已被廣泛用于醫(yī)療輔助診斷和疾病風(fēng)險(xiǎn)分析。數(shù)據(jù)預(yù)處理是機(jī)器學(xué)習(xí)的重要環(huán)節(jié)之一,在醫(yī)療體檢數(shù)據(jù)中,體檢結(jié)果往往存在個(gè)體性差異。體現(xiàn)在對(duì)于某一個(gè)特征,整個(gè)人群的特征數(shù)值分布的標(biāo)準(zhǔn)差相對(duì)較大,而且在均值以下的數(shù)量遠(yuǎn)超在均值以上的數(shù)量,表現(xiàn)為數(shù)據(jù)分布極為不平穩(wěn)。然而,傳統(tǒng)化的數(shù)據(jù)歸一化方法并不能很好的規(guī)避這一問(wèn)題。通過(guò)數(shù)學(xué)變換可以較好地解決這一問(wèn)題并在一定程度上提高模型的收斂速度以及精度。本文主要工作包括:1、提出FN(Fusion normalization)方法來(lái)進(jìn)行特征的平穩(wěn)化處理,并將特征值歸一化至(0,1)區(qū)間;2、針對(duì)多標(biāo)簽問(wèn)題,本文分別建立以SVM、GBDT、LR為基礎(chǔ)分類器的三種組合模型SVMs、GBDTs、LRs來(lái)處理醫(yī)學(xué)多標(biāo)簽數(shù)據(jù);3、針對(duì)醫(yī)學(xué)體檢指標(biāo)值正常人群數(shù)量大于異常人群數(shù)量,而造成的數(shù)據(jù)不平衡問(wèn)題,本文根據(jù)標(biāo)簽數(shù)據(jù)的比值采用對(duì)不同的標(biāo)簽設(shè)置不同的懲罰因子的方法來(lái)處理。本文的數(shù)據(jù)集包含性別、空腹血糖等62個(gè)特征,高血壓、糖尿病、脂肪肝3個(gè)標(biāo)簽。數(shù)據(jù)集中數(shù)據(jù)類型有字符型和數(shù)值型。實(shí)驗(yàn)結(jié)果表明:FN(Fusion normalization)方法處理過(guò)后的體檢數(shù)據(jù)相比于不做歸一化的的數(shù)據(jù),Max_min歸一化以及標(biāo)準(zhǔn)歸一化方法,在組合模型SVMs、GBDTs、LRs上的準(zhǔn)確率均有不同程度的提高。
[Abstract]:Health check-up is a very important part of disease prevention. Doctors can analyze the underlying symptoms on the basis of individual health check-up results, and then provide health guidance to them. According to the analysis of the health examination results, the traditional treatment method is to give the whole health condition and disease risk analysis for the experienced doctors according to the physical examination results of each part of the body. With the increasing of the data, As well as the mixed experience of doctors and so on, the artificial analysis method can not meet the increasing demand for physical examination in terms of efficiency and accuracy. With the development of data mining technology, artificial intelligence and machine learning methods have been widely used in medical assistant diagnosis and disease risk analysis. Data preprocessing is one of the important links in machine learning. In medical physical examination data, there are often individual differences in the results of physical examination. For a certain feature, the standard deviation of the distribution of the characteristic values of the whole population is relatively large, and the number below the mean value is far higher than the number above the mean value, which shows that the distribution of the data is extremely uneven. However, the traditional method of data normalization is not a good way to avoid this problem. This problem can be solved by mathematical transformation and the convergence speed and precision of the model can be improved to a certain extent. The main work of this paper is as follows: (1) the FN (Fusion normalization) method is proposed to stabilize the features and normalize the eigenvalues to (0,1); 2. Aiming at the multi-label problem, this paper establishes three combination models based on SVM,GBDT,LR classifier, SVMs,GBDTs,LRs, to deal with medical multi-label data. 3. In view of the imbalance of data caused by the number of normal population is larger than that of abnormal population, according to the ratio of label data, the method of setting different punishment factors for different labels is adopted to deal with the problem. This data set contains 62 features such as gender, fasting blood glucose, hypertension, diabetes, and fatty liver. The data types in the dataset are character type and numeric type. The experimental results show that the accuracy of the: FN (Fusion normalization) method in combination model SVMs,GBDTs,LRs is improved to some extent compared with the non-normalized data, the Max_min normalization method and the standard normalization method.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R194.3;TP18
本文編號(hào)：2440983
[Abstract]:Health check-up is a very important part of disease prevention. Doctors can analyze the underlying symptoms on the basis of individual health check-up results, and then provide health guidance to them. According to the analysis of the health examination results, the traditional treatment method is to give the whole health condition and disease risk analysis for the experienced doctors according to the physical examination results of each part of the body. With the increasing of the data, As well as the mixed experience of doctors and so on, the artificial analysis method can not meet the increasing demand for physical examination in terms of efficiency and accuracy. With the development of data mining technology, artificial intelligence and machine learning methods have been widely used in medical assistant diagnosis and disease risk analysis. Data preprocessing is one of the important links in machine learning. In medical physical examination data, there are often individual differences in the results of physical examination. For a certain feature, the standard deviation of the distribution of the characteristic values of the whole population is relatively large, and the number below the mean value is far higher than the number above the mean value, which shows that the distribution of the data is extremely uneven. However, the traditional method of data normalization is not a good way to avoid this problem. This problem can be solved by mathematical transformation and the convergence speed and precision of the model can be improved to a certain extent. The main work of this paper is as follows: (1) the FN (Fusion normalization) method is proposed to stabilize the features and normalize the eigenvalues to (0,1); 2. Aiming at the multi-label problem, this paper establishes three combination models based on SVM,GBDT,LR classifier, SVMs,GBDTs,LRs, to deal with medical multi-label data. 3. In view of the imbalance of data caused by the number of normal population is larger than that of abnormal population, according to the ratio of label data, the method of setting different punishment factors for different labels is adopted to deal with the problem. This data set contains 62 features such as gender, fasting blood glucose, hypertension, diabetes, and fatty liver. The data types in the dataset are character type and numeric type. The experimental results show that the accuracy of the: FN (Fusion normalization) method in combination model SVMs,GBDTs,LRs is improved to some extent compared with the non-normalized data, the Max_min normalization method and the standard normalization method.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R194.3;TP18
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