Ensemble learning-based type 2 diabetes kidney disease risk assessment system

Abstract

The invention discloses an ensemble learning-based type 2 diabetes kidney disease risk assessment system, which comprises a data collection module, an influence index data extraction module and an ensemble learning module, wherein the data collection module acquires a group of clinical index data of a to-be-assessed patient from a hospital database; the influence index data extraction module extracts influence index data from the group of clinical index data acquired by the data collection module; the ensemble learning module inputs the influence index data into an ensemble learning model, andthe ensemble learning model outputs a judgment result about whether the to-be-assessed patient suffers from the type 2 diabetes kidney disease or not. The system can simply, conveniently and valuablyobtain a judgment result of whether the patient suffers from the type 2 diabetes kidney disease or not, is beneficial to screening out high-risk groups with the diabetes kidney disease, helps doctorsto carry out auxiliary diagnosis, and has important significance in early diagnosis, prevention and delay of DKD, reduction of cardiovascular events, increase of the survival rate of the patient andimprovement of the life quality.

Description

technical field [0001] The invention relates to the fields of medical data analysis and integrated learning, in particular to an integrated learning-based risk assessment system for type 2 diabetic kidney disease. Background technique [0002] Diabetic kidney disease (DKD) is an important cause of chronic kidney disease (CKD) and has become one of the leading causes of end-stage renal disease (ESRD) and death in diabetic patients. Studies have shown that about 20% to 40% of diabetic patients in my country have diabetic kidney disease, and the awareness rate of diabetic kidney disease DKD is less than 20%, and the treatment rate is less than 50%. The onset of DKD is not obvious. When the disease develops to a certain stage, the main clinical manifestations are proteinuria, hypertension, edema, nephrotic syndrome and abnormal renal function. At present, the diagnosis of diabetic kidney disease DKD mainly depends on the pathological examination of renal biopsy, but the patholo...

AI Technical Summary

Problems solved by technology

Studies have shown that about 20% to 40% of diabetic patients in my country have diabetic kidney disease, and the awareness rate of diabetic kidney disease DKD is less than 20%, and the treatment rate is less than 50%.

There is no effective gold standard for patients with DKD progression tendency, which may easily lead to missed and misdiagnosed patients

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