Lung cancer diagnosis system based on multiple machine learning algorithms

Abstract

The invention discloses a lung cancer diagnosis system based on multiple machine learning algorithms, and relates to the field of medical instruments, and the system comprises a preliminary predictionmodule which is used for employing a plurality of trained lung cancer prediction models to perform lung cancer preliminary prediction processing of lung clinical data of a to-be-diagnosed patient, and obtaining a plurality of lung cancer preliminary prediction results, and a lung cancer diagnosis module used for carrying out lung cancer classification processing on the plurality of lung cancer preliminary prediction results by utilizing a trained lung cancer meta-classifier, and determining whether the patient to be diagnosed is a lung cancer patient or not. The system can be applied to the process of clinical auxiliary diagnosis of lung cancer and helps clinicians to make decisions, information can be shared to a plurality of centers through a network platform, diagnosis suggestions canbe provided for clinicians lacking experience, and the overall clinical lung cancer diagnosis level is improved.

Description

technical field [0001] The invention relates to the field of medical devices, in particular to a lung cancer diagnosis system based on various machine learning algorithms. Background technique [0002] The diagnosis of lung cancer mainly relies on auxiliary laboratory examination, chest imaging examination and pathological diagnosis. Due to the lack of typical clinical symptoms in the early stage of lung cancer, coupled with the characteristics of tumor heterogeneity, screening methods such as laboratory examinations and chest imaging examinations have certain limitations. Although histopathological examination is the gold standard for the diagnosis of lung cancer, but There are also problems such as invasiveness and operational feasibility. Therefore, the early diagnosis of lung cancer is still a problem to be solved urgently. In view of the above problems, many researchers have introduced traditional mathematical statistical models such as linear regression to assist doct...

AI Technical Summary

Problems solved by technology

Most of the data in clinical work cannot meet the above conditions. At the same time, there are many sources of clinical diagnosis data for lung cancer, which are composed of basic patient information, imaging data, laboratory data, pathological data, etc. It is relatively complicated, which makes it difficult for traditional statistical models to establish a predictive model with strong applicability when dealing with this complex disease

Images