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

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%.

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

“…Smart grids also use renewable energy resources to be safely plugged into the grid to appendage the power supply. The intelligent systems, such as Expert Systems (ESs), Fuzzy Logic (FL) [6], [7], Machine Learning (ML) [8], [9] and Deep Neural Networks (DNN) [10]- [12] have revolutionized the power distribution process. These systems render effective tools for design, simulation, fault diagnostics, and fault-tolerant control in the modern smart grid [13].…”

Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition

“…They showed that the accelerometer can also be used in combination with other sensors such as a gyroscope, light, proximity, barometer, linear acceleration and magnetometer sensor for better activity recognition. Furthermore, there is a large increase in the inventions of daily monitoring systems that can detect the user's health, lifestyle, activities, behavior, and emotions [24][25][26]. Some sensors (i.e., GPS, Microphone, Radio-frequency and Near Field Communication) are also useful in detailed health monitoring [27][28][29][30].Numerous authors have presented various approaches for activity recognition [3,[5][6][7][8][9].…”

Tree-Based Real-Time Advertisement Recommendation System in Online Broadcasting

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“…To improve the performance of the recommendation system, context information should be taken into account appropriately, according to target domains [6][7][8][9][10][11]. In the media content platform, a recommendation is mainly influenced by data generated when using the content, such as viewing history, viewing time, rating score, and feedback.…”