Background: Type 2 Diabetes Mellitus (DM) is another pandemic of 21 century, and its control is of immense importance. Researchers developed many predictor models using soft computing techniques. The present study developed a prediction model for Type 2 DM using machine learning classifiers. The analysis excludes plasma glucose concentration and insulin concentration as predictors to explore relationships with other predictors.
Methods: This cross-sectional study enrolled 108 participants aged 25 to 67 years from SMS Medical College, Jaipur (Rajasthan, India), after approval from the ethics committee. The study developed a prediction model using machine learning techniques. The classifiers used in the application include decision trees, support vector machines, K-nearest neighbors, and ensemble learning classifiers. A total of 25 predictors were collected and underwent feature reduction. The response levels include diabetes mellitus, prediabetes, and no diabetes mellitus. The models were run using three predictors and a response variable. The prediction model with the best accuracy and area under the receiver operator characteristic curve was selected.
Results: The features that vary among the three groups include age, WHR, biceps skinfold thickness, total lipids, phospholipids, triglycerides, total cholesterol, LDL, VLDL, and serum creatinine, and family history of DM. After feature reduction, the age, biceps skinfold thickness, and serum creatinine were run on the Classification learner application to predict the diabetic category. The best model was subspace discriminant with accuracy, sensitivity, specificity, and AUC under the ROC curve was 62.4%, 74%, 94%, and 0.70, respectively. Conclusion: The present study concludes that age, biceps skinfold thickness, and serum creatinine combination have higher specificity in predicting type 2 DM. The study emphasized the selection of appropriate predictors along with newer machine learning algorithms.

The life-span increase recorded during the past decades across the global population has led to an increase in the number of people suffering from dementia. Recent analyses have estimated the worldwide number of people living with Alzheimer’s disease (AD) and dementia at between 27 million and 36 million, and projections by Alzheimer’s disease International estimate that 115 million people worldwide will be living with AD/dementia by 2050, less developed countries being more affected by these rising numbers(1). Alzheimer’s disease is the most prevalent form of dementia - 60-70% cases, while vascular dementia accounts for another 25% of cases(2). Alzheimer’s disease is a neurodegenerative pathology associated with the intraneuronal accumulation of hyperphosphorylated proteins and with the extracellular aggregation of ? amyloid precursors(3). These structural modifications lead to a metabolic cascade responsible for neuronal death and, subsequently, dementia.

Chronic heart failure is a major public health problem, with increasing prevalence due to population aging and increased survival of cardiovascular patients. Chronic heart failure is a clinical syndrome characterized by a variety of effects on other organs and systems. Occasionally, patients with chronic heart failure may present with signs and symptoms of a noncardiac disorder, such as hepatic dysfunction. The main pathophysiologic mechanism involved in hepatic dysfunction of patients with heart failure is either passive congestion due to increased filling pressures or low cardiac output and the consequences of impaired perfusion. Passive hepatic congestion may lead to increase of liver enzymes and total bilirubin. Right ventricular dysfunction can be associated with severe hepatic congestion, which can be asymptomatic and revealed only by abnormal liver function tests. When hemodynamic abnormalities are prolonged, the hepatic function is further altered, with impaired coagulation tests and decreased albumin synthesis. Morphologically, the liver becomes fibrotic and ultimately cardiac cirrhosis appears. Decreased perfusion from low cardiac output may be associated with hepatocellular necrosis and increased serum aminotransferases. Acute cardiogenic liver injury appears in severe systemic hypotension due to exacerbation of heart failure
the level of aminotransferases increases, as well as lactic dehydrogenase and prothrombin time.
The aim of our study was to evaluate the frequency and the importance of liver function tests abnormalities in a group of patients with chronic heart failure, as well as the prognostic value of these liver tests.

Heart failure is a complex clinical syndrome that can be determined by any structural or functional cardiac disease that impairs the ability of the left ventricle to fill or eject blood. In developed countries, heart failure is a main cause of morbidity and mortality for adults older than 65 years, the risk of developing heart failure being increased with advancing age. Heart failure hospitalizations are associated with substantial morbidity and mortality. Understanding factors that influence clinical outcomes of patients hospitalized for heart failure, particularly the modifiable ones, may be useful for identifying strategies to improve the outcomes of these patients.
The aim of the study was to investigate the clinical characteristics, management and outcomes of patients with chronic heart failure consecutively admitted in the Internal Medicine Clinic of the Clinical Emergency Hospital of Bucharest, during a period of 8 months (1st of January - August 31, 2012).