Cerebrovascular disease neurological function damage degree prediction model based on kernel principal component analysis and polynomial characteristics

Abstract

The invention discloses a cerebrovascular disease neurological function damage degree prediction model based on kernel principal component analysis and polynomial characteristics. The method comprisesthe following steps: firstly, acquiring processed medical structured data from a big data platform, preprocessing all the data, and then adopting the following feature extraction method by firstly, constructing features, then extracting feature data with significant differences from all the feature data, then mapping the data to a high dimension by adopting kernel principal component analysis, and then reducing the dimension; and finally, constructing a machine learning model of the stroke condition severity based on logistic regression, a support vector machine and a random forest. Experiments respectively set four control groups, and it is proved that compared with the control groups, the method can effectively improve the prediction performance of each classifier on the damage degree of the neurological function of the cerebrovascular disease, and compared with the control groups, the method greatly shortens the feature extraction time.

Description

technical field [0001] The invention relates to the fields of medical big data mining and disease intelligence assessment, in particular to a prediction model for the degree of neurological damage of cerebrovascular disease based on nuclear principal component analysis and polynomial features. Background technique [0002] Cerebrovascular disease (CVD) refers to brain lesions caused by various cerebrovascular diseases, including hemorrhagic cerebrovascular disease and ischemic cerebrovascular disease, and cerebrovascular disease in a narrow sense is stroke. The burden of cerebrovascular disease in my country ranks among the top in the world. It has the characteristics of high morbidity, high disability rate, high mortality rate, and high recurrence rate. It seriously threatens population health and has become a major public health problem in my country. [0003] The clinical manifestations of neurological impairment in cerebrovascular disease are complex, and accurate assess...

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Problems solved by technology

[0004] In recent years, with the rise of medical big data, the general steps of traditional disease prediction models built by most medical units are sample acquisition→data preprocessing→integrated learner→output results, and these models rely too much on the performance of integrated learners. The potential information of mining data is not taken from the perspective of features

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