本文關(guān)鍵詞： 血糖預(yù)測(cè) 小波去噪 ARIMA BP神經(jīng)網(wǎng)絡(luò) 組合預(yù)測(cè)　出處：《鄭州大學(xué)》2015年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：隨著糖尿病患者數(shù)量的不斷增多,糖尿病對(duì)人類(lèi)健康的危害日趨增加,而穩(wěn)定血糖是糖尿病患者臨床治療的主要目的,如果能提前預(yù)測(cè)出患者的血糖濃度,那么醫(yī)生和患者就能在高血糖或者低血糖事件發(fā)生之前采取措施來(lái)穩(wěn)定血糖,這將極大減小糖尿病對(duì)患者造成的傷害。建立一個(gè)精確度比較高的血糖預(yù)測(cè)模型,為醫(yī)生和糖尿病患者提供指導(dǎo),具有很好的應(yīng)用價(jià)值。目前,關(guān)于人體血糖預(yù)測(cè)技術(shù)的研究大體有兩個(gè)方向:一個(gè)方向是只利用患者的歷史血糖值,不考慮影響患者血糖動(dòng)態(tài)變化的外部因素(飲食、藥物注射、運(yùn)動(dòng)等),追求簡(jiǎn)單和高效,但不夠精準(zhǔn);另外一個(gè)方向不僅利用糖尿病患者的歷史血糖值,而且結(jié)合人體的生理模型和大量的病理學(xué)、生理學(xué)的知識(shí),追求準(zhǔn)確和精準(zhǔn),算法復(fù)雜,有一定延遲。本文深入研究了影響人體血糖變化的關(guān)鍵因素和血糖預(yù)測(cè)所面臨的問(wèn)題,在比較了現(xiàn)有的血糖預(yù)測(cè)技術(shù)的基礎(chǔ)上,探討基于ARIMA和BPNN的組合預(yù)測(cè)模型對(duì)患者血糖未來(lái)值分析和預(yù)測(cè)的可行性。采用ARIMA對(duì)糖尿病患者的歷史血糖值進(jìn)行分析,找出患者血糖變化的線性規(guī)律,利用BPNN捕獲外部因素對(duì)人體血糖的影響,并對(duì)輸入值、誤差項(xiàng)等進(jìn)行學(xué)習(xí)和擬合。最后將ARIMA計(jì)算出的預(yù)測(cè)值與BP算法得到的修正值進(jìn)行組合,得到準(zhǔn)確的結(jié)果。同時(shí),針對(duì)飲食或藥物注射在短時(shí)間內(nèi)對(duì)人體血糖波動(dòng)的突發(fā)影響,設(shè)定開(kāi)始影響的點(diǎn)為奇異點(diǎn),提出一種奇異點(diǎn)發(fā)現(xiàn)和處理算法,在人體血糖受外部干擾發(fā)生不規(guī)律變化時(shí)自動(dòng)調(diào)整未來(lái)一段時(shí)間內(nèi)的預(yù)測(cè)值,保證組合預(yù)測(cè)模型的精度和準(zhǔn)確度。采用河南省人民醫(yī)院內(nèi)分泌科所提供的糖尿病患者血糖數(shù)據(jù)對(duì)所提出來(lái)的基于ARIMA和BPNN的組合預(yù)測(cè)模型及奇異點(diǎn)發(fā)現(xiàn)和處理算法進(jìn)行驗(yàn)證。結(jié)果表明,相比ARIMA預(yù)測(cè),所提出來(lái)的組合預(yù)測(cè)模型具有更好的預(yù)測(cè)效果,可以給醫(yī)生或者糖尿病患者提供臨床上的指導(dǎo);所提出的奇異點(diǎn)發(fā)現(xiàn)和處理算法,在人體血糖受外部干擾發(fā)生急劇變化時(shí)能自動(dòng)調(diào)整未來(lái)一段時(shí)間內(nèi)的預(yù)測(cè)值,能保證組合預(yù)測(cè)模型的預(yù)測(cè)精度和準(zhǔn)確度。
[Abstract]:With the increasing number of patients with diabetes, diabetes is increasingly harmful to human health, and stable blood sugar is the main purpose of clinical treatment of patients with diabetes, if we can predict the concentration of blood sugar in advance. Then doctors and patients can take steps to stabilize blood sugar before hyperglycemia or hypoglycemia occurs, which will greatly reduce the damage caused by diabetes. To provide guidance for doctors and patients with diabetes, has a good application value. At present, there are two directions in the study of blood glucose prediction technology: one direction is to use only the patient's historical blood sugar value. Regardless of the external factors (diet, drug injection, exercise, etc.) that affect the dynamic changes of blood glucose, the pursuit of simplicity and efficiency, but not accurate; The other direction not only uses the historical blood sugar value of diabetic patients, but also combines the physiological model of the human body and a lot of pathology, physiological knowledge, the pursuit of accuracy and precision, complex algorithm. In this paper, the key factors affecting the changes of blood glucose and the problems of blood glucose prediction are studied in depth, based on the comparison of existing blood glucose prediction techniques. To explore the feasibility of the combination prediction model based on ARIMA and BPNN to analyze and predict the future value of blood glucose in patients with diabetes mellitus. ARIMA was used to analyze the historical blood glucose value of patients with diabetes mellitus. To find out the linear rule of blood glucose change, use BPNN to capture the influence of external factors on human blood sugar, and to input value. Finally, the predicted value of ARIMA is combined with the revised value of BP algorithm to get accurate results. Aiming at the sudden effect of diet or drug injection on blood glucose fluctuation in a short period of time, the singularity point is set as the starting point, and a singular point detection and processing algorithm is proposed. Automatically adjust the predicted value for a period of time when the body's blood sugar changes irregularly by external interference. To ensure the accuracy and accuracy of the combined prediction model. The combined prediction model based on ARIMA and BPNN was proposed by using the blood glucose data of diabetic patients provided by the Endocrinology Department of Henan Provincial people's Hospital. The algorithm of point discovery and processing is verified. The results show that. Compared with ARIMA prediction, the proposed combined prediction model has better predictive effect and can provide clinical guidance to doctors or patients with diabetes. The proposed singular point detection and processing algorithm can automatically adjust the prediction value in the future when the blood sugar changes sharply by external interference, which can ensure the prediction accuracy and accuracy of the combined prediction model.
【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】：2015
【分類(lèi)號(hào)】：R587.1;TP18

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本文編號(hào)：1476947