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Abstract Cardiovascular diseases have risen to become one of the most common health problems in both developing and developed countries. Using data mining techniques, this study was designed to develop a risk level prediction model and clinical decision support system for CVD in Ethiopia. Visual C# Studio's Visual C# studio software created the prototype system. The introduced prototype system helps health care professionals to recognize risk level CVD diseases. This research's future research will include expanding the model by gathering more datasets and changing the default setting of WEKA, a data mining software.
Source link: https://doi.org/10.21203/rs.3.rs-1944769/v1
Methods We performed a cross-sectional research and multivariable logistic regression analysis to help clarify the connection between inflammation markers and metabolic disorders and CVD risk. To assess 10-year CVD risk, we used the Framingham risk score to assess 10-year CVD risk. Results Odds ratios for MetD, 2. 091 for MetS, 1. 547 for dyslipidemia, 1. 547 for dyslipidemia, and 1. 515 for central obesity were recorded for the third tertile of MHR, 1. 53 for MetD, 2. 091 for MetS, 1. 547 for dyslipidemia, 1. 547 for central obesity, and 1. 515 for central obesity. MetD's ratios for the third tertile of SIRI were 2. 092 for MetD, 3. 441 for MetS, 1. 417 for dyslipidemia, and 2. 080 for central obesity, based on the third vs. the first tertile of SIRI were 2. 092 for MetD, 3. 441 for MetS, 1. 417 for dyslipidemia, and 2. 080 for central obesity. For the third vs. the first tertile of MHR, the odds ratio was 0. 76 to 3. 397 for SIRI. Conclusions MHR and SIRI had a strong link with MetD and its constituents, in which a higher risk of metabolic disease was associated with a higher risk of metabolic diseases.
Source link: https://doi.org/10.3389/fendo.2022.944991
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