Discrimination Analysis' Application In Ovarian Cyst Early Stage Discrimination Diagnosis And Its Software Development

Objective: At the moment, the multivariate analysis statistics means is more and more used in the quantitative diagnosis of clinical disease and its type home and abroad, Yet in the quantitative diagnosis of specific disease type, only one kind of multivariate analysis means was mostly used .In this research, in order to improve the accuracy and effectiveness of discrimination diagnosis of disease, three kinds of multivariate analysis means were used to discriminate early stage ovarian cyst and programs were developed for the application of the three kinds of multivariate analysis means that were used in the ovarian cyst early stage discrimination. Methods: 1.the method of maximum likelihood:with the method of maximum likelihood, the conditional probability of every index symptom and its score were computed according to many clinical examination indexes of ovarian cyst, in view of which to establish the quantitative diagnosis table of ovarian cyst early stage discrimination.2.Information analysis :Based on the principle of information analysis and the theorem of information analysis' application on the value of clinical disease diagnosis, the information source entropy of the two types of ovarian cyst and the total information amount of every symptom were computed, in view of which the contribution rate of every symptom were computed and the symptoms which contribution rate were relatively smaller were removed, then the information entropy of symptom reserved were computed, in view of which the quantitative diagnosis table of ovarian cyst early stage discrimination were established.3. Logistic regression analysis:Firstly, collinearity diagnosis was made on logistic regression model, if there was collinearity between the independent variable; logistic regression based on principal component analysis was selected. After logistic regression based on principal component analysis, the significant examination indexes for ovarian cyst discrimination was screened and the logistic regression equation was established, after analysis of the fit of logistic regression model, based on which establishing the logistic linearity regression equation which used on logistic discrimination analysis was established to realize the ovarian cyst early stage discrimination. 4 Software development : Software development applies Delphi7.0 that was produced by American Borland International Inc.The process of program development mainly was consisted of the establishment of DB on discrimination table, the design and establishment of application program interface, the programming the function of discrimination and the open of application programmed. Results:1.the method of maximum likelihood :The retrospective discrimination coincidence rate is 83.57%, redundant discrimination coincidence rate is 85.03%, non-redundant discrimination coincidence rate is 82.42%;the prospective discrimination coincidence rate is 82.43%,redundant discrimination coincidence rate is 80%,non-redundant discrimination coincidence rate is 84.09%.2.Information analysis:the retrospective discrimination coincidence rate is 89.05%,redundant discrimination coincidence rate is 86.39 %,non-redundant discrimination coincidence rate is 90.48%; the prospective discrimination coincidence rate is 87.84%,redundant discrimination coincidence rate is 86.67%,non-redundant discrimination coincidence rate is 88.64%.3.Logistic regression analysis:After logistic regression based on principal component analysis, the logistic linearity regression equation is =0.228 age+0.698 personal history-0.950 menorrhalgia history+0.405 parity+0.883 cyst surface+1.265 inside echo-1.287 compression symptom+1.244 thick of wall-0.734 cyst chamber-0.423 abortion number+0.906 cyst size-3.977. The area under ROC curve is 0.868, the std.error is 0.018, p=0.000,p<0.05,which show that the model has the moderate forecast ability. The retrospective discrimination coincidence rate is 87.86%, redundant discrimination coincidence rate is 85.71%, non-redundant discrimination coincidence rate i...