Method for diagnosing keratoconus case based on XGBoost+SVM hybrid machine learning
The invention provides a method for diagnosing a keratoconus case based on XGBoost+SVM hybrid machine learning. The method includes the following steps that: the corneal examination data of ophthalmicpatients are collected, each corneal sample is labeled with a category label keratoconus, suspected...
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| creator | WANG SHUHANG XU JIAHUI PEI LEQI CUI TONG ZHANG LIN JI SHUFAN WANG YAN |
| description | The invention provides a method for diagnosing a keratoconus case based on XGBoost+SVM hybrid machine learning. The method includes the following steps that: the corneal examination data of ophthalmicpatients are collected, each corneal sample is labeled with a category label keratoconus, suspected keratoconus and normal cornea by ophthalmologists, and the labeled corneal samples are adopted as training sample data; the feature values of the features of the corneal sample data are normalized and are mapped to an interval [0, 1]; the XKoost is used to expand the features of the sample data, and an expanded feature set is adopted as the training features of the samples; and based on the training features of the sample data, an SVM diagnosis model is trained and constructed; and the diagnosis model is adopted to diagnose and predict new cases. Tests have shown that the diagnostic effect of the method has met the requirements of clinical applications. The method can be used for screeningthe keratoconus, especiall |
| format | Patent |
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The method includes the following steps that: the corneal examination data of ophthalmicpatients are collected, each corneal sample is labeled with a category label keratoconus, suspected keratoconus and normal cornea by ophthalmologists, and the labeled corneal samples are adopted as training sample data; the feature values of the features of the corneal sample data are normalized and are mapped to an interval [0, 1]; the XKoost is used to expand the features of the sample data, and an expanded feature set is adopted as the training features of the samples; and based on the training features of the sample data, an SVM diagnosis model is trained and constructed; and the diagnosis model is adopted to diagnose and predict new cases. Tests have shown that the diagnostic effect of the method has met the requirements of clinical applications. 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The method includes the following steps that: the corneal examination data of ophthalmicpatients are collected, each corneal sample is labeled with a category label keratoconus, suspected keratoconus and normal cornea by ophthalmologists, and the labeled corneal samples are adopted as training sample data; the feature values of the features of the corneal sample data are normalized and are mapped to an interval [0, 1]; the XKoost is used to expand the features of the sample data, and an expanded feature set is adopted as the training features of the samples; and based on the training features of the sample data, an SVM diagnosis model is trained and constructed; and the diagnosis model is adopted to diagnose and predict new cases. Tests have shown that the diagnostic effect of the method has met the requirements of clinical applications. 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| language | chi ; eng |
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| subjects | CALCULATING COMPUTING COUNTING HANDLING RECORD CARRIERS HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA IMAGE DATA PROCESSING OR GENERATION, IN GENERAL INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
| title | Method for diagnosing keratoconus case based on XGBoost+SVM hybrid machine learning |
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