Chronic obstructive pulmonary disease testing system based on machine learning

Abstract

The invention discloses a chronic obstructive pulmonary disease testing system based on machine learning. The system comprises a lung function detector, a processor and a display unit; the lung function detector is used for obtaining function detection items of lungs of a person to be tested and measurement values of the items; the processor is connected with the lung function detector and comprises a main component feature analysis module, a decision tree construction module and a decision tree testing module; the display unit is connected with the processor and used for outputting results of the processor; the chronic obstructive pulmonary disease testing system based on the machine learning is based on a machine learning model based on a decision tree, a relationship between a chronic obstructive pulmonary disease and all physical signs of lungs of a patient is established, and the system has the advantages of being high in testing accuracy, strong in reliability and more stable.

Description

technical field [0001] The invention relates to the field of medical data mining, in particular to a chronic obstructive pulmonary disease testing system constructed by using a machine learning method. Background technique [0002] Airflow limitation is the most basic feature of chronic obstructive pulmonary disease (COPD). Pulmonary function tests are of great significance in assessing the degree of airflow limitation, and are the most widely used and most reproducible routine tests for COPD patients. At present, there is no published technical information on the COPD testing system based on the relationship between the physiological indicators of lung function and the degree of airflow limitation. At present, it is usually determined by the forced expiratory volume in one second (FEV1) and the ratio of FEV1 to forced vital capacity (FVC) (FEV1 / FVC). In addition, the second maximum expiratory flow-volume curve (MEFV), peak expiratory flow (PEF), ratio of residual volume (...

AI Technical Summary

Problems solved by technology

However, in practice, it is found that COPD patients with similar physical and chemical indicators above have different clinical manifestations, different pathological changes, different airway inflammation and systemic inflammatory status, different quality of life and different disease prognosis

At present, based on data mining and machine learning related theories, a COPD test system that explores the correlation between physical and chemical indicators of lung function and COPD has not yet appeared.

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