A machine learning method for heart disease prediction using convolutional neural network

Heart disorder is one of the complicated sicknesses and globally many human beings suffered from this disorder. On time and green identity of coronary heart disorder a key function in fitness care especially withinside the area of cardiology. The utility of disorder prediction the usage of system gaining knowledge of withinside the scientific analysis area is growing successively. This may be contributed mainly to the development withinside the type and pinpointing structures utilized in disorder identity and reputation structures utilized in disorder analysis that's capable of offer records that aids health workers in early identity of deadly sicknesses and therefore, elevating the survival price of sufferers importantly. Applying distinctive styles of algorithms, every with its personal benefit on 3 separate databases of disorder (Heart) to be had in UCI repository for disorder prognosis. The results regarding the identification of diseases using machine learning algorithm has strengthen the concept of the applying of machine learning in early detection of diseases, so that the disease can be diagnosed in the early stage itself and then it can be treated as earlier.So that the survival prices of the sufferers may be increased.&nbsp

A study on the correlation between serum levels of fibroblast growth factor-19 and cardiovascular risk factors in patients with metabolic syndrome

Metabolic  syndrome  is  a  major  global  threat  nowadays  due  to  urbanization,  sedentary  life  style  and  increased  incidence  of  obesity. FGF-19  has  recently  been  introduced  as  a  novel  marker regulating  metabolism,   reversing  diabetes  mellitus,  hyper  lipidemia,  hepatic  steatosis  and adiposity. Aim & Objective: To compare the serum Fibroblast Growth Factor 19 levels of metabolic syndrome patients with healthy individuals. To analyze the correlation between serum FGF 19 and the components of metabolic syndrome. Materials & Methods: A total of 50 patients and 50 controls were included in the study. After obtaining informed consent, anthropometric measures (Height, Weight, BMI & Waist circumference) were taken. Blood investigations such as FGF 19, TC, HDL-cholesterol were estimated and LDL, VLDL & AIP levels were calculated. Statistical Analysis: Student’s t-test was employed for the statistical analysis and data were expressed in terms of mean and standard deviation. ‘p’ value less than 0.05 is considered as statistically significant. Correlation between the measured parameters was assessed using Pearson’s correlation coefficient. Result & Conclusion: Serum levels of FGF 19 were low in patients with metabolic syndrome