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Realizing an Efficient IoMT-Assisted Patient Diet Recommendation System Through Machine Learning Model | IEEE Journals & Magazine | IEEE Xplore

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


Recommender System Classi?cation Report for Machine Learning and Deep Learning Models Using different algorithms such as naive byes, logistic regression Multilayer Percep...

Abstract:

Recent studies have shown that robust diets recommended to patients by Dietician or an Artificial Intelligent automated medical diet based cloud system can increase longe...Show More
Topic: Deep Learning Algorithms for Internet of Medical Things

Abstract:

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%.
Topic: Deep Learning Algorithms for Internet of Medical Things
Recommender System Classi?cation Report for Machine Learning and Deep Learning Models Using different algorithms such as naive byes, logistic regression Multilayer Percep...
Published in: IEEE Access ( Volume: 8)
Page(s): 28462 - 28474
Date of Publication: 21 January 2020
Electronic ISSN: 2169-3536

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