Investigation of Nutritional Status of Children based on Machine Learning Techniques using Indian Demographic and Health Survey Data

Khare, Sangita; Kavyashree, S; Gupta, Deepa; Jyotishi, Amalendu

doi:10.1016/j.procs.2017.09.087

Cited by 62 publications

(33 citation statements)

References 12 publications

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“…Integrating machine learning techniques in predicting patient survival and disease status has become increasingly popular in healthcare and public health research [11,[15][16][17]35] resulting in a positive impact on the improvement of health care planning. However, till date, very little research has been done on the use of machine learning algorithms to predict the disease status using cross-sectional demographic and health survey data [36,37]. Moreover, no research has explored the potential of ML in predicting anemia status of children underfive years in Bangladesh.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Algorithms To Predict The Childhood Anemia In Bangladesh

Khan¹,

Chowdhury²,

Islam³

et al. 2021

Journal of Data Science

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Anemia, especially among children, is a serious public health problem in Bangladesh. Apart from understanding the factors associated with anemia, it may be of interest to know the likelihood of anemia given the factors. Prediction of disease status is a key to community and health service policy making as well as forecasting for resource planning. We considered machine learning (ML) algorithms to predict the anemia status among children (under five years) using common risk factors as features. Data were extracted from a nationally representative cross-sectional survey-Bangladesh Demographic and Health Survey (BDHS) conducted in 2011. In this study, a sample of 2013 children were selected for whom data on all selected variables was available. We used several ML algorithms such as linear discriminant analysis (LDA), classification and regression trees (CART), k-nearest neighbors (k-NN), support vector machines (SVM), random forest (RF) and logistic regression (LR) to predict the childhood anemia status. A systematic evaluation of the algorithms was performed in terms of accuracy, sensitivity, specificity, and area under the curve (AUC). We found that the RF algorithm achieved the best classification accuracy of 68.53% with a sensitivity of 70.73%, specificity of 66.41% and AUC of 0.6857. On the other hand, the classical LR algorithm reached a classification accuracy of 62.75% with a sensitivity of 63.41%, specificity of 62.11% and AUC of 0.6276. Among all considered algorithms, the k-NN gave the least accuracy. We conclude that

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Section: Discussionmentioning

confidence: 99%

Machine Learning Algorithms To Predict The Childhood Anemia In Bangladesh

Khan¹,

Chowdhury²,

Islam³

et al. 2021

Journal of Data Science

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show abstract

“…For instance, individualized recommendations about meal plans could be one of the ways to promote healthier food-consumption behaviors (76). In addition, modeling algorithms can be designed as assistive technologies for the pediatrician to help them better detect at-risk patients and (a) advise parents or guardian regarding nutrition enhancement (77), or (b) use healthcare resources including breastfeeding and vaccinations on the most vulnerable population more effectively (78).…”

Section: Malnutrition Assessment Tools and Models In Clinical Settingsmentioning

confidence: 99%

“…AI could be designed to enhance the quality of care through early diagnosis, effective and personalized care plans, and risk identification and mitigation in some patients (79). Studies on integration of technology have shown that algorithms can be developed to recommend individualized meal plans for the elderly (76) as well as provide advice on nutritional enhancement and healthcare resources for children (77,78). Further, well designed and validated algorithms can collect and mine immense amounts of longitudinal patient data from electronic health records, including body mass index, blood pressure, and body composition to predict potential clinical outcomes and recommendations for patients affected by obesity or malnutrition (84).…”

Section: Modeling-enabled Nutritional Assessment Strategiesmentioning

confidence: 99%

Malnutrition, Health and the Role of Machine Learning in Clinical Setting

Sharma

Khan

et al. 2020

Front. Nutr.

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Nutrition plays a vital role in health and the recovery process. Deficiencies in macronutrients and micronutrients can impact the development and progression of various disorders. However, malnutrition screening tools and their utility in the clinical setting remain largely understudied. In this study, we summarize the importance of nutritional adequacy and its association with neurological, cardiovascular, and immunerelated disorders. We also examine general and specific malnutrition assessment tools utilized in healthcare settings. Since the implementation of the screening process in 2016, malnutrition data from hospitalized patients in the Geisinger Health System is presented and discussed as a case study. Clinical data from five Geisinger hospitals shows that ∼10% of all admitted patients are acknowledged for having some form of nutritional deficiency, from which about 60-80% of the patients are targeted for a more comprehensive assessment. Finally, we conclude that with a reflection on how technological advances, specifically machine learning-based algorithms, can be integrated into electronic health records to provide decision support system to care providers in the identification and management of patients at higher risk of malnutrition.

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“…Another important area of application with particular relevance to the poor is the detection and prevention of malnutrition, which is one of the leading causes of infant mortality in developing countries. Khare et al (2017) designed a prediction model for malnutrition based on a machine learning approach, using the available features in the Indian Demographic and Health Survey (IDHS) dataset. Their findings suggest that this approach identifies some important features that had not been detected by the existing literature.…”

Section: Health Services For the Poor Facilitated By Ai/roboticsmentioning

confidence: 99%

AI/Robotics and the Poor

Braun¹,

Baumüller²

2021

Robotics, AI, and Humanity

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Artificial intelligence and robotics (AI/R) have the potential to greatly change livelihoods. Information on how AI/R may affect the poor is scarce. This chapter aims to address this gap in research. A framework is established that depicts poverty and marginality conditions of health, education, public services, work, small businesses, including farming, as well as the voice and empowerment of the poor. This framework identifies points of entry of AI/R, and is complemented by a more detailed discussion of the way in which changes through AI/R in these areas may relate positively or negatively to the livelihood of the poor. Context will play an important role determining the AI/R consequences for the diverse populations in poverty and marginalized populations at risk. This chapter calls for empirical scenarios and modelling analyses to better understand the different components in the emerging technological and institutional AI/R innovations and to identify how they will shape the livelihoods of poor households and communities.

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Investigation of Nutritional Status of Children based on Machine Learning Techniques using Indian Demographic and Health Survey Data

Cited by 62 publications

References 12 publications

Machine Learning Algorithms To Predict The Childhood Anemia In Bangladesh

Machine Learning Algorithms To Predict The Childhood Anemia In Bangladesh

Malnutrition, Health and the Role of Machine Learning in Clinical Setting

AI/Robotics and the Poor

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