Intelligent Nutrition in Healthcare and Continuous Care

Miranda, Rui; Ferreira, Diana; Abelha, António; Machado, José

doi:10.1109/ceap.2019.8883496

Cited by 5 publications

(4 citation statements)

References 17 publications

(20 reference statements)

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“…For this purpose, three levels of quality have been defined: High (H) the articles with a score between 8 and 11, Medium (M) between 5.1 and 7.9, and Low (L) less than 5. After the qualification, we can affirm that within the metrics used in this study, the articles [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] are of high impact.…”

Section: Resultsmentioning

confidence: 71%

Systematic Review of Nutritional Recommendation Systems

2021

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In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society. There are many apps created to count calories based on what we eat, or to estimate calorie consumption according to the sport we do, or to recommend recipes, but very few are capable of giving personalized recommendations. This review tries to see what studies exist and what recommendation systems are used for this purpose, over the last 5 years in the main databases. Among the results obtained, it is observed that the existing works focus on the recommendation system (usually collaborative filtering), and not so much on the description of the data or the sample analyzed; the indices used for the calculation of calories or nutrients are not specified. Therefore, it is necessary to work with open data, or well-described data, which allows the experience to be reproduced by third parties, or at least to be comparable. In recent years, the promotion of healthy habits, and especially diet-oriented habits, has been one of the priority interests of our society.

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Section: Resultsmentioning

confidence: 71%

Systematic Review of Nutritional Recommendation Systems

2021

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“…Its goal is to provide kids from the age of eight to their early teens with a healthier eating lifestyle which varies according to their age, gender, and other related factors. The study of the authors in ( 9 ) suggested a platform that smartly plans the meals of users according to their health conditions. This platform makes use of machine learning algorithms to function.…”

Section: Related Workmentioning

confidence: 99%

Blockchain-Secured Recommender System for Special Need Patients Using Deep Learning

Mantey

Zhou

Anajemba

et al. 2021

Front. Public Health

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Recommender systems offer several advantages to hospital data management units and patients with special needs. These systems are more dependent on the extreme subtle hospital-patient data. Thus, disregarding the confidentiality of patients with special needs is not an option. In recent times, several proposed techniques failed to cryptographically guarantee the data privacy of the patients with special needs in the diet recommender systems (RSs) deployment. In order to tackle this pitfall, this paper incorporates a blockchain privacy system (BPS) into deep learning for a diet recommendation system for patients with special needs. Our proposed technique allows patients to get notifications about recommended treatments and medications based on their personalized data without revealing their confidential information. Additionally, the paper implemented machine and deep learning algorithms such as RNN, Logistic Regression, MLP, etc., on an Internet of Medical Things (IoMT) dataset acquired via the internet and hospitals that comprises the data of 50 patients with 13 features of various diseases and 1,000 products. The product section has a set of eight features. The IoMT data features were analyzed with BPS and further encoded prior to the application of deep and machine learning-based frameworks. The performance of the different machine and deep learning methods were carried out and the results verify that the long short-term memory (LSTM) technique is more effective than other schemes regarding prediction accuracy, precision, F1-measures, and recall in a secured blockchain privacy system. Results showed that 97.74% accuracy utilizing the LSTM deep learning model was attained. The precision of 98%, recall, and F1-measure of 99% each for the allowed class was also attained. For the disallowed class, the scores were 89, 73, and 80% for precision, recall, and F1-measure, respectively. The performance of our proposed BPS is subdivided into two categories: the secured communication channel of the recommendation system and an enhanced deep learning approach using health base medical dataset that spontaneously identifies what food a patient with special needs should have based on their disease and certain features including gender, weight, age, etc. The proposed system is outstanding as none of the earlier revised works of literature described a recommender system of this kind.

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“…This is quite different from our own AI model. Meanwhile, the patients' attitude is also important according to [31] but the most important is Transparency and Traceability in food supply chain as to determine which product is suitable for the patient and choice of food to prevent poisoning [32], [33], [35], [35]. Certainly, the resultant effect of busy life styled-patients may assume unhealthy diets.…”

Section: Related Workmentioning

confidence: 99%

Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

et al. 2020

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Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longevity, protect against further disease, and improve the overall quality of life. However, medical personnel are yet to fully understand patient-dietician's rationale of recommender system. This paper proposes a deep learning solution for health base medical dataset that automatically detects which food should be given to which patient base on the disease and other features like age, gender, weight, calories, protein, fat, sodium, fiber, cholesterol. This research framework is focused on implementing both machine and deep learning algorithms like, logistic regression, naive bayes, Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). The medical dataset collected through the internet and hospitals consists of 30 patient's data with 13 features of different diseases and 1000 products. Product section has 8 features set. The features of these IoMT data were analyzed and further encoded before applying deep and machine and learning-based protocols. The performance of various machine learning and deep learning techniques was carried and the result proves that LSTM technique performs better than other scheme with respect to forecasting accuracy, recall, precision, and F1-measures. We achieved 97.74% accuracy using LSTM deep learning model. Similarly 98% precision, 99% recall and 99% F1-measure for allowed class is achieved, and for not-allowed class precision is 89%, recall score is 73% and F1 Measure score is 80%.

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Intelligent Nutrition in Healthcare and Continuous Care

Cited by 5 publications

References 17 publications

Systematic Review of Nutritional Recommendation Systems

Systematic Review of Nutritional Recommendation Systems

Blockchain-Secured Recommender System for Special Need Patients Using Deep Learning

Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model

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