In today's digital world, a dataset with large number of attributes has a curse of dimensionality where the computation time grows exponentially with the number of dimensions. To overcome the problem of computation time and space, appropriate method of feature selection can be developed using metaheuristic approaches. The aim of this work is to investigate the use of ant colony optimization with the help of neural network to select near optimal feature subset and integrate it with the self-organizing fuzzy logic classifier for improving the recognition rate. The proposed fuzzy classifier derives prototype from the collected data through an offline training process and uses it to develop a fuzzy inference system for classification. Once trained, it can continuously learn from streaming data and later adapts the changing facts by updating the system structure recursively. The developed model is not based on predefined parameters used in the data generation model but is derived from the empirically observed data.

<p>Diabetes mellitus is a chronic disease that affects many people in the world badly. Early diagnosis of this disease is of paramount importance as physicians and patients can work towards prevention and mitigation of future complications. Hence, there is a necessity to develop a system that diagnoses type 2 diabetes mellitus (T2DM) at an early stage. Recently, large number of studies have emerged with prediction models to diagnose T2DM. Most importantly, published literature lacks the availability of multi-class studies. Therefore, the primary objective of the study is development of multi-class predictive model by taking advantage of routinely available clinical data in diagnosing T2DM using machine learning algorithms. In this work, modified mayfly-support vector machine is implemented to notice the prediabetic stage accurately. To assess the effectiveness of proposed model, a comparative study was undertaken and was contrasted with T2DM prediction models developed by other researchers from last five years. Proposed model was validated over data collected from local hospitals and the benchmark PIMA dataset available on UCI repository. The study reveals that modified Mayfly-SVM has a considerable edge over metaheuristic optimization algorithms in local as well as global searching capabilities and has attained maximum test accuracy of 94.5% over PIMA.</p>

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Introduction: Silent myocardial ischemia is defined as objective evidence of myocardial ischemia without angina or angina equivalent. In Framingham study, 30% of myocardial infarcts were silent, diagnosed only by serial electrocardiography. There is increasing evidence that asymptomatic myocardial infarctions and silent ischemia occur more frequently in diabetic patients. So present study was done to record the prevalence of silent myocardial ischemia in asymptomatic patients of type 2 diabetes mellitus. Material and methods: The present study was a cross sectional study, carried out among 88 randomly selected patients of type 2 diabetes mellitus for more than 1 year, patients >18 years of age, who do not have any symptoms related to cardiovascular system. Results: Our study shows that the prevalence of silent MI among patients with DM was 20.45%. It was reported in the present study that the mean fasting and post prandial blood sugar among patients was 162.23±12.18 mg/dl and 192.4±21.29 mg/dl respectively. Conclusion: Our study shows that the prevalence of silent MI among patients with DM was 20.45%. In our study majority of cases of silent myocardial ischemia were found in patients with age group of >50 years, patients with duration of diabetes mellitus of 5-10 years.