Approved Research

Data mining for cardiovascular and cerebrovascular diseases diagnosis discrimination in the UK Biobank

Lay summary

Cardiovascular and cerebrovascular diseases are major causes of mortality and are worldwide public health problem. Several factors, such as aging, hypertension, diabetes or dyslipidemia are associated with these diseases but are not specific.

Currently, no single correct diagnosis approach exits for patients in cardiovascular and cerebrovascular diseases prediction due to the variability in different clinical symptoms of patients and imperfection of diagnosis from noninvasive and invasive tests.

The objectives of our work were to use intelligence artificial to improve the accuracy decision diagnosis of cardiovascular and cerebrovascular diseases using all potential related risk factors. Data mining models could be interesting tools for prediction of Cardiovascular and cerebrovascular diseases and could participate to the detection of performed discrimination of cardiovascular and cerebrovascular diseases diagnosis. Data mining focuses machine learning, statistical analysis and databank technology. It assists the medical practitioner and analyst to mark intelligent medical decision which outmoded support system cannot.

The in-depth analysis of cardiovascular risk data will be spread over a three-year period to presenting several interesting results, particularly in Cardiovascular and cerebrovascular diseases.

Applied software production could be an interesting tool for cardiovascular and cerebrovascular diseases prediction. These models could be utilized such as a predictive tool in a personalized medicine in cardiovascular and cerebrovascular diseases diagnosis and prediction. Many studies will be needed to better evaluate these models and then estimate their acceptance by clinicians.