Abstract
Depression is always the core field of psychological research, and the analysis of misdiagnosis data of depression is also the vital content of depression research. Based on the analysis of misdiagnosis data processing, this paper adopts a order relation analysis method, to correct the problem of inconsistent entropy and entropy transfer relation (when all entropy value tend to be 1). This paper obtains multi-index comprehensive quantitative values, from various angles analysis of misdiagnosis data depression, so as to avoid subjective and one-sided evaluation results. It not only improves the rapidity and practicability of the algorithm, but also makes the analysis of misdiagnosis data more objective and accurate, which can be applied to medical field.
Highlights
Depression disorder has always been one of the frontburner issue in the field of psychological research, the analysis of misdiagnosis data of depression is the core content of depression research
2.1 data processing of clinical symptoms by modified entropy weight method and sequence relation analysis method: Standardization of membership matrix of evaluation indexes: The membership evaluation matrix is constructed by constructing the index values of n evaluated objects corrers p ond⋯ing r to m evaluation indexes R: R= ⋮ ⋱ ⋮
We divided the different clinical symptoms into cognitive psychological symptoms and physiological neurological symptoms, and subdivided them into ten secondary indexes, respectively, anxiety, emotional fluctuation, neural inhibition, retardation of thinking, slow movements, loss of interest, the circulatory system, the digestive system, the respiratory system, the nervous system.[4]According to the data of symptoms, the modified entropy weight method was substituted into the formula to calculate the relevant weights, so as to observe which symptoms accounted for the greater weight in clinical manifestations, so as to facilitate the analysis
Summary
Depression disorder has always been one of the frontburner issue in the field of psychological research, the analysis of misdiagnosis data of depression is the core content of depression research. There are many comprehensive evaluation and analysis methods which are mostly studying on depression on risk factors and prediction models based on statistical, including entropy weight method, artificial neural network method, analytic hierarchy process, logistic regression model design, fuzzy mathematics method and its combination method (cross-sectional and longitudinal studies). These multi-objective fuzzy comprehensive analysis methods have the limitation of artificial assignment of weight of traits, making evaluation results less scientific. 2.1.1 Calculate the index entropyHH aaaaaa wwwwwwwwwww ww w 0 1 H . w H . w w
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