Automatic classification method for eye refraction correction multi-source data based on XGBoost

A multi-source data and data classification technology, applied in the field of machine learning algorithms in medical data processing, can solve problems such as unbalanced sample size, achieve the effects of improving classification results, shortening parameter optimization time, and avoiding data coupling

Pending Publication Date: 2020-07-14
王雁
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Problems solved by technology

The difficulty of applying this scheme is that the sample size of each data type is seriously unbalanced, and the data types include characters, numbers and other

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  • Automatic classification method for eye refraction correction multi-source data based on XGBoost
  • Automatic classification method for eye refraction correction multi-source data based on XGBoost
  • Automatic classification method for eye refraction correction multi-source data based on XGBoost

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Embodiment Construction

[0032] The present invention will be further described in detail below through the specific examples, the following examples are only descriptive, not restrictive, and cannot limit the protection scope of the present invention with this.

[0033] An XGBoost-based automatic classification method for eye refraction multi-source data, specifically comprising the following steps:

[0034] Step 1: Preprocess the raw data. It includes operations such as data screening, numericalization, labeling, and division of training sets and test sets. The following specific instructions (steps 1.1-1.3):

[0035] Step 1.1 digitizes the statistically obtained data and cleans up abnormal data.

[0036] Step 1.2 performs standardization processing and scale transformation processing such as normalization on the data, wherein the refraction-related data is converted into LogMAR (international standard logarithmic visual acuity) data to make it linear.

[0037] In step 1.3, the data is randomly div...

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Abstract

The invention relates to an automatic classification method for eye refraction correction multi-source data based on XGBoost, and the method comprises the steps: selecting an attribute feature relatedto eye refraction data classification as the most original feature for training by adopoting a scheme of combining the clinical experience of an ophthalmologist with a statistical strategy; based onthe screened data, utilizing an XGBoost algorithm to further perform feature screening according to the feature importance, and selecting related attribute features most related to the target; and based on the selected training samples, considering the problem of sample imbalance, giving different weights to each sample and avoiding setting corresponding early stop functions by training over-fitting, and training an XGBoost model to classify the samples. According to the method, the accuracy of multi-source data classification can be effectively improved, manual intervention is not needed in the training process, the training time is shortened, and the training efficiency is improved.

Description

technical field [0001] The invention belongs to the field of machine learning algorithms applied to medical data processing, and relates to machine learning technology, in particular to an algorithm scheme for automatically classifying multi-source data of corneal refraction correction in ophthalmology by using an integrated learning method based on an XGBoost model. Background technique [0002] Myopia has become the leading cause of visual impairment in the world. It has not only become one of the focus medical issues of global concern, but also an important social issue. Every year, a large number of people need myopia correction, so it is necessary to find a safe, effective and accurate correction method. At present, corneal refractive surgery is the main method for correcting myopia among young people. In China, more than one million people receive refractive surgery every year. Corneal refractive surgery includes a variety of surgical methods. The selection of differe...

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Application Information

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IPC IPC(8): G06K9/62G06N20/00G16H20/40G16H50/70
CPCG06N20/00G16H20/40G16H50/70G06F18/24155G06F18/24323
Inventor 王雁马娇楠孟祥冰
Owner 王雁
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