Cancer recurrence prediction system based on multi-dimensional Gaussian distribution Bayesian classification

Abstract

The invention relates to a cancer recurrence prediction system based on a multi-dimensional Gaussian distribution Bayesian classification, which comprises a preprocessing module, a training module anda Bayesian classifier; the pre-processing module performs data cleaning on the training set and generates a class vector data set; the training module first calculates the first probability of two class attributes, and then divides the data attribute into a class data attribute set which is closely related to the class attribute and a class II data attribute set which is sparse with the class attribute association degree by using the pearson correlation coefficient, two types of data attribute sets are respectively used for calculating a corresponding probability by using a multi-dimensionalGaussian distribution and a one-dimensional Gaussian distribution; the Bayesian classifier combines both the probability of the two and the first probability of the class together as the probability of the data belonging to each class, and the classification test result of the cancer is judged accordingly. The cancer recurrence prediction system based on multi-dimensional Gaussian distribution Bayesian classification improves the predictive accuracy of the recurrence of the cancer.

Description

technical field [0001] The invention belongs to the technical field of data mining, and relates to a multidimensional Gaussian distribution Bayesian classification system based on attribute selection, in particular to a cancer recurrence prediction system based on multidimensional Gaussian distribution Bayesian classification. The system has universal applicability to the general classification of continuous data satisfying Gaussian distribution. Background technique [0002] Classification method is a method to solve classification problems, and it is an important research field in data mining, machine learning and pattern recognition. The classification method finds classification rules by analyzing the training set of known categories, so as to predict the category of new data. Classification methods are used in a wide range of applications, such as risk assessment in banking, customer category classification, text retrieval and search engine classification, intrusion de...

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Problems solved by technology

Once breast cancer recurs or metastasizes, the difficulty of treatment will increase, which is far more difficult than the first treatment, and it is likely to directly threaten the patient's life

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