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Publication date: 20 May 2022

Magesh Nagarajan and Patturaja Selvaraj

The purpose of this study is to evaluate the efficiency of the relative performances of Mother’s canteen across the regions of Tamil Nadu and find out the determinants of…

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Abstract

Purpose

The purpose of this study is to evaluate the efficiency of the relative performances of Mother’s canteen across the regions of Tamil Nadu and find out the determinants of inefficiencies in the scheme.

Design/methodology/approach

An untargeted food security scheme called Amma (Mother's) canteen was started in Tamil Nadu, India, with an aim to provide the urban poor with hygienic and healthy food at an affordable price. Along with secondary data, interviews were conducted to understand the operational details of Mother's canteen. Data envelopment analysis (DEA) was used to find the relative efficiency of the scheme operated by nine corporations.

Findings

Based on the daily expenditure, number of meals served and revenue, seven of nine corporations were found to be inefficient. Further, sensitivity analyses found that among six procurement variables, procurement (quantity and price) of black gram and cooking oil were determinants of inefficiency.

Research limitations/implications

As an untargeted scheme, the cost of delivering service-based evaluation was used for performance evaluation. Policymakers could use centralized procurement instead of open market procurement at the corporation level and standardized ingredients' usage (quantity) to further reduce the cost of the food security scheme.

Practical implications

The proposed DEA model may be used by policymakers to empirically evaluate the food security scheme's delivery effectiveness across various corporations in a region. Inefficient branches are identified here with empirical support for further performance improvement changes.

Originality/value

There are limited number of studies evaluating untargeted schemes. This paper presents the challenges of evaluating an untargeted scheme which allows self-selection of beneficiaries. The outcome of this study will help in identifying inefficient corporations, and further, improve the performance and cost of delivering untargeted food security scheme.

Details

Benchmarking: An International Journal, vol. 30 no. 4
Type: Research Article
ISSN: 1463-5771

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Article
Publication date: 11 November 2021

Sandeep Kumar Hegde and Monica R. Mundada

Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio…

136

Abstract

Purpose

Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio vasculardisease (CVD) and chronic kidney disease (CKD) are major chronic diseases responsible for millions of death. Each of these diseases is considered as a risk factor for the other two diseases. Therefore, noteworthy attention is being paid to reduce the risk of these diseases. A gigantic amount of medical data is generated in digital form from smart healthcare appliances in the current era. Although numerous machine learning (ML) algorithms are proposed for the early prediction of chronic diseases, these algorithmic models are neither generalized nor adaptive when the model is imposed on new disease datasets. Hence, these algorithms have to process a huge amount of disease data iteratively until the model converges. This limitation may make it difficult for ML models to fit and produce imprecise results. A single algorithm may not yield accurate results. Nonetheless, an ensemble of classifiers built from multiple models, that works based on a voting principle has been successfully applied to solve many classification tasks. The purpose of this paper is to make early prediction of chronic diseases using hybrid generative regression based deep intelligence network (HGRDIN) model.

Design/methodology/approach

In the proposed paper generative regression (GR) model is used in combination with deep neural network (DNN) for the early prediction of chronic disease. The GR model will obtain prior knowledge about the labelled data by analyzing the correlation between features and class labels. Hence, the weight assignment process of DNN is influenced by the relationship between attributes rather than random assignment. The knowledge obtained through these processes is passed as input to the DNN network for further prediction. Since the inference about the input data instances is drawn at the DNN through the GR model, the model is named as hybrid generative regression-based deep intelligence network (HGRDIN).

Findings

The credibility of the implemented approach is rigorously validated using various parameters such as accuracy, precision, recall, F score and area under the curve (AUC) score. During the training phase, the proposed algorithm is constantly regularized using the elastic net regularization technique and also hyper-tuned using the various parameters such as momentum and learning rate to minimize the misprediction rate. The experimental results illustrate that the proposed approach predicted the chronic disease with a minimal error by avoiding the possible overfitting and local minima problems. The result obtained with the proposed approach is also compared with the various traditional approaches.

Research limitations/implications

Usually, the diagnostic data are multi-dimension in nature where the performance of the ML algorithm will degrade due to the data overfitting, curse of dimensionality issues. The result obtained through the experiment has achieved an average accuracy of 95%. Hence, analysis can be made further to improve predictive accuracy by overcoming the curse of dimensionality issues.

Practical implications

The proposed ML model can mimic the behavior of the doctor's brain. These algorithms have the capability to replace clinical tasks. The accurate result obtained through the innovative algorithms can free the physician from the mundane care and practices so that the physician can focus more on the complex issues.

Social implications

Utilizing the proposed predictive model at the decision-making level for the early prediction of the disease is considered as a promising change towards the healthcare sector. The global burden of chronic disease can be reduced at an exceptional level through these approaches.

Originality/value

In the proposed HGRDIN model, the concept of transfer learning approach is used where the knowledge acquired through the GR process is applied on DNN that identified the possible relationship between the dependent and independent feature variables by mapping the chronic data instances to its corresponding target class before it is being passed as input to the DNN network. Hence, the result of the experiments illustrated that the proposed approach obtained superior performance in terms of various validation parameters than the existing conventional techniques.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 1
Type: Research Article
ISSN: 1756-378X

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