Abstract Healthcare is one of the world’s fastest growing industries, having large volumes of data collected on a daily basis. It is generally perceived as being ‘information rich’ yet ‘knowledge poor’. Hidden relationships and valuable knowledge can be discovered in the collected data from the application of data mining techniques. These techniques are being increasingly implemented in healthcare organizations in order to respond to the needs of doctors in their daily decision-making activities. To help the decision-makers to take the best decision it is fundamental to develop a solution able to predict events before their occurrence. The aim of this project was to predict if a patient would need to be followed by a nutrition specialist, by combining a nutritional dataset with data mining classification techniques, using WEKA machine learning tools. The achieved results showed to be very promising, presenting accuracy around 91%, specificity around 97% and precision about 95%.

Machine Learning in Nutritional Follow-up Research

The evaluation of food intake is important in scientific research and clinical practice to understand the relationship between diet and health conditions of an individual or a population. Large volumes of data are generated daily in the health sector. In this sense, Artificial Intelligence (AI) tools have been increasingly used, for example, the application of Machine Learning (ML) algorithms to extract useful information, find patterns, and predict diseases. This systematic review aimed to identify studies that used ML algorithms to assess food intake in different populations. A literature search was conducted using five electronic databases, and 36 studies met all criteria and were included. According to the results, there has been a growing interest in the use of ML algorithms in the area of nutrition in recent years. Also, supervised learning algorithms were the most used, and the most widely used method of nutritional assessment was the food frequency questionnaire. We observed a trend in using the data analysis programs, such as R and WEKA. The use of ML in nutrition is recent and challenging. Therefore, it is encouraged that more studies are carried out relating these themes for the development of food reeducation programs and public policies.

Applicability of machine learning techniques in food intake assessment: A systematic review.

Malnutrition among women is a major public health problem that has been linked to stunted growth, diabetes, and has adverse consequences for children, including low birth weight, less resistance to infections, and a higher risk of death. This current study presents an exhaustive comprehensive study of machine learning (ML) system which has two major objectives: (i) identification of the potential risk factors of malnourished women; and (ii) propose a better ML-based model for predicting malnourished women. About 15,464 respondents were taken from the Bangladesh Demographic and Health Survey. The potential risk factors for malnutrition were extracted using multinomial logistic regression (MLR). Five ML-based algorithms such as Naïve Bayes, support vector machine, decision tree, artificial neural network, and random forest (RF) were implemented for predicting malnourished women and evaluating their performances using accuracy and area under the curve (AUC). MLR illustrates that age, region, wealth index, respondent's education, currently breastfeeding and pregnant women, marital status, toilet facility, and cooking fuel are the potential risk factors for underweight, while age, region, residence, wealth index, respondent's and husband's education, currently breastfeeding and working, children's ever-born, drinking water source, and cooking fuel for overweight/obese. Our findings show that RF-based classifier provides 81.4% accuracy and 0.837 AUC for underweight and 82.4% accuracy and 0.853 AUC for overweight/obese. The combination of MLR-RF-based system could accurately classify malnourished women with higher accuracy. The proposed system will be helpful in predicting which women are at high risk of malnutrition and reducing the burden of the health system. 

Application of machine learning based algorithm for prediction of malnutrition among women in Bangladesh

Dans cette these nous analysons dans quelle mesure le concept de mobilite peut etre pris en compte dans la demarche d'analyse et de conception de systemes de production. Notre apport vise a formaliser la demarche d'analyse et de conception de ce dernier, explicitant les decisions a prendre, les informations necessaires et les criteres de decision a mettre en place. Dans cet objectif, deux niveaux d'analyse ont ete distingues : un niveau local concernant un site de production et un niveau global comprenant un ensemble de sites.Le premier niveau local considere un seul site de production. A ce niveau nous avons propose une approche prenant en compte les caracteristiques du site de production. Dans notre contexte, le choix de la localisation geographique de production est impose par le client. De ce fait, la conception du systeme de production doit s'adapter a cette contrainte. D'un point de vue conception, quatre questions sont abordees : (1) dans quelle mesure le concept de mobilite peut etre integre dans une demarche de conception de systeme de production mobile ? (2) quelles caracteristiques de l'environnement de production doivent etre prises en compte ? (3) comment determiner ce qu'il faut produire sur site ou ce qu'il serait opportun d'externaliser ? et (4) compte tenu des informations obtenues quelle est la meilleure configuration du SPM a envisager et selon quels criteres de choix ? La reponse a ces questions conduit a la proposition d'une configuration du SPM adaptee pour un seul site de production.Le deuxieme niveau global traite la problematique de mobilite successive multi sites. En effet, pour etre rentabilise le systeme de production doit etre mobilise sur plusieurs sites de production. A chaque changement de site de production, une reconfiguration du systeme de production s'impose en se basant sur la configuration existante (version i-1). LaThese de Youssef BENAMAreconfigurabilite concerne d'une part l'architecture interne du systeme (choix des machines, recrutement de nouvelles equipes locales, etc) et d'autre part l'organisation de la chaine d'approvisionnement du SPM (faire en interne ou externaliser, fournisseur local, etc.). A ce niveau global d'analyse, nous proposons deux modeles d'analyse : (1) un premier modele pour l'analyse de la reconfigurabilite interne. Ce modele d'analyse permet d'adapter le nombre de lignes de production et le nombre d'equipes en fonction d'un scenario de demande (localisations geographiques, capacite necessaire par site). L'originalite de notre proposition consiste d'une part en l'evaluation des couts de reconfiguration necessaires et d'autre part l'appreciation du niveau d'adequation de la configuration proposee avec le contexte du site de production via l'utilisation de l'indicateur de mobilite. (2) Le deuxieme modele d'analyse concerne la reconfigurabilite de la chaine d'approvisionnement amont du SPM. Il consiste en une adaptation du modele d'aide a la decision "faire ou faire faire" par l'integration d'un cote de l'importance du site de production et d'un autre cote des specificites de chaque site de production.La demarche d'analyse proposee est illustree sur le cas industriel concernant la conception d'une usine mobile pour la fabrication et l'installation sur site de composants de centrales solaires thermodynamiques.

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Formalisation de la démarche de conception d'un système de production mobile : intégration des concepts de mobilité et de reconfigurabilité

Proper nutrition is an essential component for the survival, growth, and development of children in society. Malnutrition is a global problem in today's life. The primary target of this phenomenon is children under five years of age and mostly for developing countries. In this study, a data mining approach is proposed to predict the malnutrition status of children under five years of age in Afghanistan. Random Forest, PART rule induction, and Naive Bayes classifiers were applied to Afghanistan Nutrition SMART Survey data. The results were compared with Logistic Regression statistical method. Random Forest and PART rule induction classifiers performed well with the highest accuracy for stunted, underweight, wasted, and nutritional oedema statuses with and without transformed attributes. This study defines how data mining classification techniques can classify malnutrition status for children under five years of age. Generally, our method was the one that obtained the most robust results to predict the malnutrition status based on clinical sign and anthropometric parameters of preschool-age Afghan children.

Data Mining Based Prediction of Malnutrition in Afghan Children

The assessment of health status in communities throughout the world is a massive information technology challenge. Data warehousing provides a flexible environment to support the business management and serve as an integrated repository for data. With the addition of models and analytic tools that have the potential to provide actionable information resources and support effective problem identification, critical decision-making, and strategy formulation, implementation, and evaluation. Of particular interest are the factors of influence like the patient's height or weight and its impact on processes and results. A multidimensional process is a way to discover health care processes according to certain factors of influence. This study aims to implement a data warehousing environment for decision support, in the context of nutrition evaluation, to integrate data obtained from a health care facility. This paper highlights the implementation of Business Intelligence in health care settings allows searching and interpreting stored information to support decisions concerning people's life.

Business intelligence for nutrition therapy

GlioBastoma Multiforme (GBM) is an aggressive primary brain tumor characterized by a heterogeneous cell population that is genetically unstable and resistant to chemotherapy. Indeed, despite advances in medicine, patients diagnosed with GBM have a median survival of just one year. Magnetic Resonance Imaging (MRI) is the most widely used imaging technique for determining the location and size of brain tumors. Indisputably, this technique plays a major role in the diagnosis, treatment planning, and prognosis of GBM. Therefore, this study proposes a new Case Based Reasoning approach to problem solving that attempts to predict a patient’s GBM volume after five months of treatment based on features extracted from MR images and patient attributes such as age, gender, and type of treatment.

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Rita Reis

Papers

Machine Learning in Nutritional Follow-up Research

Business intelligence for nutrition therapy

A Case-Based Reasoning Approach to GBM Evolution