Application method of machine learning classification model in adolescent autism auxiliary diagnosis

A technology for auxiliary diagnosis and classification models, applied in machine learning, computer-aided medical procedures, computing models, etc., can solve problems such as time-consuming, cost-consuming, and prone to randomness

Inactive Publication Date: 2020-04-14
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
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Problems solved by technology

The former is easily affected by the doctor's subjective factors, resulting in misdiagnosis
In addition, if the abnormal behavior of children is only observed in a short time window, it is prone to randomness
However, if the time window is enlarged, there will be the same problem as the latter, that is, it will take more time, and there are certain deficiencies in reliability and timeliness.
The development of brain imaging technologies such as magnetic resonance allows doctors to obtain more and faster patient data, but it is still not possible to diagnose directly through magnetic resonance images

Method used

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  • Application method of machine learning classification model in adolescent autism auxiliary diagnosis
  • Application method of machine learning classification model in adolescent autism auxiliary diagnosis
  • Application method of machine learning classification model in adolescent autism auxiliary diagnosis

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Embodiment Construction

[0080] The following will be combined with Figure 1-Figure 5 The present invention is described in detail, and the technical solutions in the embodiments of the present invention are clearly and completely described. Apparently, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0081] The present invention provides an application method of a machine learning classification model in the auxiliary diagnosis of autism in adolescents through improvement; it is implemented in the following manner;

[0082] (1) Model training methods; including Hold-out, CrossValidation and Bootstrapping;

[0083] The set-out method divides the initial data set D into two sub-data sets S and T, satisfying: D=S∪T and Train the model M o...

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Abstract

The invention discloses an application method of a machine learning classification model in adolescent autism auxiliary diagnosis. The method is characterized by being implemented according to the following mode. The method comprises the steps of 1, establishing a model training method; 2, constructing a model evaluation index; 3, performing characteristic engineering of the autism auxiliary diagnosis system; 4, performing data dimension reduction processing; 5, carrying out feature selection; and 6, carrying out model training and result analysis. According to the invention, a machine learning method is introduced into the field of autism research; the high efficiency and the reliability brought by the method are greatly helpful to the auxiliary diagnosis of autism. The application fieldof the invention can be embodied in: (1) disease diagnosis and treatment, (2) smoking addiction, network addiction and network game addiction, (3) cognition and other health fields, and the like.

Description

technical field [0001] The invention relates to the field of application of machine learning classification models, in particular to an application method of machine learning classification models in auxiliary diagnosis of autism in adolescents. Background technique [0002] The current main diagnostic method for autism is still very dependent on the doctor's clinical experience. Doctors make the diagnosis by observing children for certain specified abnormal behaviors or by taking a comprehensive and detailed growth history, medical history, and mental examination. The former is easily affected by the doctor's subjective factors, resulting in misdiagnosis. In addition, if the abnormal behavior of children is only observed in a short time window, it is prone to randomness. However, if the time window is enlarged, there will be the same problem as the latter, that is, it will consume more time, and there will be certain deficiencies in terms of reliability and timeliness. T...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/20G16H20/70G06K9/62G06N20/00
CPCG16H50/20G16H20/70G06N20/00G06F18/2132G06F18/2135G06F18/24147G06F18/259G06F18/25G06F18/214
Inventor 邢建川丁志新杨骁王翔李悦王天翼
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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