Predication model establishing method based on naive Bayesian algorithm

Abstract

The invention particularly relates to a predication model establishing method based on a naive Bayesian algorithm. The predication model establishing method based on the naive Bayesian algorithm comprises the steps of collecting factor data related with cerebral apoplexy, converting qualitative data to quantitative data, calculating a correlation coefficient between each feature and a target valueby means of a Pearson correlation coefficient method, and quantitatively converting the correlation coefficient number to the weight; respectively processing discrete feature data and continuous feature data according to a traditional algorithm, a polynomial and a Gaussian model, lifting the influence of important features to a predication result by means of a weighted feature analysis method, and furthermore obtaining the predication model with high predication accuracy. According to the predication model establishing method based on the naive Bayesian algorithm, the predication model with high predication accuracy is finally obtained by means of a hybrid predication model, a feature weighting method, a sliding defining factor and a plurality of evaluation indexes; reference data can besupplied for clear diagnosis and treatment of a doctor; and furthermore an important meaning is realized for development of a national health industry.

Description

technical field [0001] The present invention relates to the technical field of machine learning algorithms, in particular to a method for establishing a predictive model based on the naive Bayesian algorithm. Background technique [0002] Stroke is an acute cerebrovascular disease, which can cause death in severe cases. Stroke itself lacks effective treatment methods, and it has the characteristics of "four highs": high morbidity, high disability, high mortality, and high recurrence. Therefore, how to achieve timely early warning and prevention of diseases and provide data basis for doctors' definite diagnosis and treatment is of great significance to the development of public health. [0003] The Naive Bayesian model is a classic supervised learning machine learning algorithm. It is proposed based on the Bayesian formula. It is already a fast and effective classification algorithm for the binary classification problem of disease or not. However, since Naive Bayes has diff...

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Problems solved by technology

However, since Naive Bayes has different optimal implementation models for different feature types, and the algorithm itself does not consider the correlation factors between features and target results, there is still room for improvement in the algorithm itself, and its classification prediction effect is very good. are also largely directly influenced by the characteristics of their selection

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