本文關(guān)鍵詞：粗糙集屬性約簡方法在醫(yī)療診斷中的應(yīng)用研究　出處：《蘇州大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 粗糙集 屬性約簡 啟發(fā)規(guī)則 遺傳算法 醫(yī)療診斷

【摘要】：如今,醫(yī)院積存了海量的醫(yī)療診斷數(shù)據(jù),如何利用先進的數(shù)據(jù)處理技術(shù)對其開發(fā)利用,輔助醫(yī)生診斷,已經(jīng)成為當(dāng)今醫(yī)療事業(yè)發(fā)展的一個重要方向。目前醫(yī)療診斷的主要方法是根據(jù)患者的癥狀進行臨床診斷。但隨著疾病種類的增多,癥狀之間的干擾性大大增強,這給醫(yī)生帶來很大負擔(dān)。本文主要針對醫(yī)療診斷問題及粗糙集屬性約簡算法進行研究,提出了兩種屬性約簡算法輔助醫(yī)療診斷。主要工作如下:1)針對HORAFA算法以及現(xiàn)有改進算法所獲得的結(jié)果中經(jīng)常出現(xiàn)屬性權(quán)值相同的問題,本文結(jié)合醫(yī)療診斷數(shù)據(jù)特征,改進算法的啟發(fā)規(guī)則及屬性刪除操作,提出一種改進的基于差別矩陣的啟發(fā)式約簡算法。實驗結(jié)果表明,改進算法能夠提高約簡效率,獲得更優(yōu)約簡。2)為了解決大規(guī)模醫(yī)療診斷數(shù)據(jù)的約簡問題,提出一種自適應(yīng)遺傳約簡算法。該算法利用改進的屬性權(quán)值構(gòu)造個體適應(yīng)度函數(shù);使用新的最優(yōu)選擇策略豐富群體種類,避免陷入局部極值;引入屬性相似度概念減少交叉操作,且降低了適應(yīng)度函數(shù)的計算次數(shù);改進變異操作,避免個體中存在權(quán)值相同的屬性。仿真實驗表明,該算法在屬性數(shù)目繁多、數(shù)據(jù)量龐大的信息系統(tǒng)中更適用。3)基于上述的遺傳約簡算法,對2型糖尿病數(shù)據(jù)進行屬性約簡,提取關(guān)鍵癥狀,輔助醫(yī)生診斷,減少誤診和漏診。
[Abstract]:Now, the hospital has accumulated a large amount of medical diagnosis data, how to use the advanced data processing technology to develop and use to assist doctors in diagnosis. At present, the main method of medical diagnosis is to make clinical diagnosis according to the symptoms of the patients. But with the increase of the types of diseases, the interference between the symptoms is greatly enhanced. This brings a great burden to doctors. This paper mainly focuses on medical diagnosis problem and rough set attribute reduction algorithm. Two attribute reduction algorithms are proposed to assist medical diagnosis. The main work is as follows: 1) aiming at the problem of the same attribute weight value in the results of HORAFA algorithm and existing improved algorithm. In this paper, an improved heuristic reduction algorithm based on discriminant matrix is proposed by combining the features of medical diagnosis data, the heuristic rules and attribute deletion operations of the algorithm are improved. The improved algorithm can improve the efficiency of reduction and obtain better reduction. 2) in order to solve the problem of large-scale medical diagnosis data reduction. An adaptive genetic reduction algorithm is proposed, in which the individual fitness function is constructed by using the improved attribute weights. The new optimal selection strategy is used to enrich population types and avoid falling into local extremum. The concept of attribute similarity is introduced to reduce crossover operations and to reduce the number of computation of fitness function. The simulation results show that the algorithm is more suitable in the information system with a large number of attributes and large amount of data. 3) based on the above genetic reduction algorithm. The data of type 2 diabetes were reduced, the key symptoms were extracted, the diagnosis was assisted, and the misdiagnosis and missed diagnosis were reduced.
【學(xué)位授予單位】：蘇州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：R4;TP18

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