Clinico-microbiological analysis of a series of 25 patients with culture proven melioidosis was done. All patients came from the coastal regions of Kerala and Karnataka and presented between June 2005 to July 2006. They were analysed with respect to clinical presentation, occupation, epidemiology and microbiological features. No single presenting clinical feature was found to be typical of melioidosis. The disease was found to mimic a variety of conditions, including tuberculosis and malignancy. Burkholderia pseudomallei was isolated from blood, sputum, pus, urine, synovial, peritoneal and pericardial ß uids. Diabetes mellitus was the most common predisposing factor and 80% of the cases presented during the Southwest monsoon (June to September). It is probable that melioidosis is highly prevalent in western coastal India and yet, greatly underestimated. Better awareness, both among clinicians and microbiologists, coupled with improved diagnostic methods to allow early diagnosis and hence early treatment, will signiÞ cantly reduce the morbidity and mortality associated with this disease.
The field of biosciences have advanced to a larger extent and have generated large amounts of information from Electronic Health Records. This have given rise to the acute need of knowledge generation from this enormous amount of data. Data mining methods and machine learning play a major role in this aspect of biosciences. Chronic Kidney Disease(CKD) is a condition in which the kidneys are damaged and cannot filter blood as they always do. A family history of kidney diseases or failure, high blood pressure, type 2 diabetes may lead to CKD. This is a lasting damage to the kidney and chances of getting worser by time is high. The very common complications that results due to a kidney failure are heart diseases, anemia, bone diseases, high potasium and calcium. The worst case situation leads to complete kidney failure and necessitates kidney transplant to live. An early detection of CKD can improve the quality of life to a greater extent. This calls for good prediction algorithm to predict CKD at an earlier stage . Literature shows a wide range of machine learning algorithms employed for the prediction of CKD. This paper uses data preprocessing,data transformation and various classifiers to predict CKD and also proposes best Prediction framework for CKD. The results of the framework show promising results of better prediction at an early stage of CKD
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