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More than two billion people suffer from micronutrient deficiencies (MiNDs) globally, with nearly half living in India. The current risk of ‘hidden hunger’ is severe in India due to serious deficiency risks across an array of essential micronutrients. A nationwide advisory board meeting attended by more than 20 Indian health care professionals (HCPs) was conducted to determine their clinical viewpoint on MiND. An in-depth search of PubMed studies emphasizing various aspects of MiND relevant to the Indian scenario was performed and presented to eminent HCPs from across India who then shared their opinions and perspectives based on their clinical experiences associated with MiND.

There is little information on comprehensive diabetes care comprising glycaemic, lipid, and blood pressure control in India; therefore, we aimed to assess the achievement of treatment targets among adults with self-reported diabetes.The Indian Council of Medical Research (ICMR)-India Diabetes (INDIAB) study is a cross-sectional, population-based survey of adults aged 20 years or older in all 30 states and union territories of India. We used a stratified multistage sampling design, sampling states in a phased manner, and selected villages in rural areas and census enumeration blocks in urban areas. We used a three-level stratification method on the basis of geography, population size, and socioeconomic status for each state. For the outcome assessment, good glycaemic control was defined as HbABetween Oct 18, 2008, and Dec 17, 2020, 113 043 individuals (33 537 from urban areas and 79 506 from rural areas) participated in the ICMR-INDIAB study. For this analysis, 5789 adults (2633 in urban areas and 3156 in rural areas) with self-reported diabetes were included in the study population. The median age was 56·1 years (IQR 55·7-56·5). Overall, 1748 (weighted proportion 36·3%, 95% CI 34·7-37·9) of 4834 people with diabetes achieved good glycaemic control, 2819 (weighted proportion 48·8%, 47·2-50·3) of 5698 achieved blood pressure control, and 2043 (weighted proportion 41·5%, 39·9-43·1) of 4886 achieved good LDL cholesterol control. Only 419 (weighted proportion 7·7%) of 5297 individuals with self-reported diabetes achieved all three ABC targets, with significant heterogeneity between regions and states. Higher education, male sex, rural residence, and shorter duration of diabetes (10 years) were associated with better achievement of combined ABC targets. Only 951 (weighted proportion 16·7%) of the study population and 227 (weighted proportion 36·9%) of those on insulin reported using self-monitoring of blood glucose.Achievement of treatment targets and adoption of healthy behaviours remains suboptimal in India. Our results can help governments to adopt policies that prioritise improvement of diabetes care delivery and surveillance in India.Indian Council of Medical Research and Department of Health Research, Ministry of Health and Family Welfare.

Background: The classification method is required to deduce possible errors and assist the doctors. These methods are used to take suitable decisions in real world applications. It is well known that classification is an efficient, effective and broadly utilized strategy in several applications such as medical diagnosis, etc. The prime objective of this research paper is to achieve an efficient and effective classification method for Diabetes. Methods: The proposed methodology comprises two phases: The first phase deals with t h e description of Pima Indian Diabetes Dataset and Localized Diabetes Dataset, whereas in the second phase, the dataset has been processed through two different approaches. Results: The first approach entails classification through Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel and Linear Kernel SVM on Pima Indian Diabetes Dataset and Localized Diabetes Dataset. In the second approach, PSO has been utilized as a feature reduction method followed by using the same set of classification methods used in the first approach. PSO_Linear Kernel SVM provides the highest accuracy and ROC for both the above mentioned dataset. Conclusion: The present work consists of a comparative analysis of outcomes w.r.t. performance assessment has been done PSO and without PSO for the same set of classification methods. Finally, it has been concluded that PSO selects the relevant features, reduces the expense and computation time while improving the ROC and accuracy. The used methodology could be implemented in other medical diseases.

Background:The modern society is extremely prone to many life-threatening diseases, which can be easily controlled as well as cured if diagnosed at an early stage. The development and implementation of a disease diagnostic system have gained huge popularity over the years. In the current scenario, there are certain factors such as environment, sedentary lifestyle, genetic (hereditary) are the major factors behind the life threatening diseases such as ‘diabetes.’ Moreover, diabetes has achieved the status of the modern man’s leading chronic disease. So one of the prime needs of this generation is to develop a state-of-the-art expert system which can predict diabetes at a very early stage with a minimum of complexity and in an expedited manner. The primary objective of this work is to develop an indigenous and efficient diagnostic technique for detection of diabetes.Method & Discussion:The proposed methodology comprises of two phases: In the first phase The Pima Indian Diabetes Dataset (PIDD) has been collected from the UCI machine learning repository databases and Localized Diabetes Dataset (LDD) has been gathered from Bombay Medical Hall, Upper Bazar Ranchi, Jharkhand, India. In the second phase, the dataset has been processed through two different approaches. The first approach entails classification through Adaboost, Classification via Regression (CVR), Radial Basis Function Network (RBFN), K-Nearest Neighbor (KNN) on Pima Indian Diabetes Dataset and Localized Diabetes Dataset. In the second approach, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have been applied as a feature reduction method followed by using the same set of classification methods used in the first approach. Among all of the implemented classification methods, PCA_CVR achieves the maximum performance for both the above mentioned datasets.Conclusion:In this article, comparative analysis of outcomes obtained by with and without the use of PCA and LDA for the same set of classification method has been done w.r.t performance assessment. Finally, it has been concluded that PCA & LDA both are useful to remove the insignificant features, decreasing the expense and computation time while improving the ROC and accuracy. The used methodology may similarly be applied to other medical diseases.

In this modern era, one of the prime most facilities available to this generation is state-of-the-art health care, and still diabetes has emerged as one the leading chronic disease. Diabetes is a condition which implies the glucose level is more than the inquisitive level on a managed premise. The prime motto of this study is to provide a good classification of diabetes. There are existing methods, which are for the classification of diabetes popularly datasets “Pima Indian Diabetes Dataset.” Here, the proposed work comprises of four phases: In the first stage, a “Localized Diabetes Dataset” has been compiled and collected from Bombay Medical Hall, Upper Bazar Ranchi, India. In the second stage, neural networks has been used as the classification technique on localized diabetes dataset. In the third stage, GA has been used as a feature selection technique through which six features among twelve features have been obtained. Lastly in the fourth stage, neural networks have been used for classification on suitable attributes produced by GA. In this study, the results have been compared with and without GA for used classification technique. It has been concluded in this work that GA is helpful in removing not only significant attributes, deducing the cost and computation time but also enhancing the ROC and accuracy. The utilized strategy may likewise be executed in other medical issues.

Background: In this era of cutting edge research, though one of the ubiquitous facilities accessible to modern man is state of the art medical care yet diabetes has emerged as one of the major ailments afflicting the present generation. So the prime necessity of this age has transformed into providing cheap and sustainable medical care against such major diseases like diabetes. In layman’s terms Diabetes may be defined as a physiological condition wherein the blood glucose level is more than the prescribed level on a regular basis. Objectives: So the prime objective of this work is to provide a novel classification technique for detection of diabetes in a timely and effective manner. Methods: The proposed work comprises of four phases: In the first phase a “Localized Diabetes Dataset” has been compiled and collected from Bombay Medical Hall, Mahabir Chowk, Pyada Toli, Upper Bazar, Jharkhand, Ranchi, India. In the second phase various classification techniques namely RBF NN, MLP NN, NBs, and J48graft DT have been applied on the Localized Diabetes Dataset. In the third phase, Genetic algorithm (GA) has been utilized as an attribute selection technique through which six attributes among twelve attributes have been filtered. Lastly in the fourth phase RBF NN, MLP NN, NBs and J48graft DT has been utilized for classification on relevant attributes obtained by GA. Results: In this study, comparative analysis of outcomes obtained by with and without the use of GA for the same set of classification technique has been done w.r.t performance assessment. It has been conclusively inferred that GA is helpful in removing insignificant attributes, reducing the cost and computation time while enhancing ROC and accuracy. Conclusion: The utilized strategy may likewise be executed for other medical issues.