An intelligent mobile diabetes management and educational system for Saudi Arabia: System architecture

Alotaibi, Mohammed; Istepanian, R.S.H.; Sungoor, Ala; Philip, Nada

doi:10.1109/bhi.2014.6864296

Cited by 13 publications

(8 citation statements)

References 3 publications

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“…Our study is highlighting many of the societal challenges that can be faced in introducing digital health interventions in the younger Arab population. There are previous studies in the region, but they have focused on a much narrower implementation of eHealth technologies [ 29 ] and specially focusing on diabetes and obesity in the adult population [ 30 , 31 ]. Another novelty of our study is the combination of social media as an additional source of health quantified-self data.…”

Section: Discussionmentioning

confidence: 99%

Implementing 360° Quantified Self for childhood obesity: feasibility study and experiences from a weight loss camp in Qatar

Fernández-Luque

Singh

Ofli

et al. 2017

BMC Med Inform Decis Mak

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BackgroundThe explosion of consumer electronics and social media are facilitating the rise of the Quantified Self (QS) movement where millions of users are tracking various aspects of their daily life using social media, mobile technology, and wearable devices. Data from mobile phones, wearables and social media can facilitate a better understanding of the health behaviors of individuals. At the same time, there is an unprecedented increase in childhood obesity rates worldwide. This is a cause for grave concern due to its potential long-term health consequences (e.g., diabetes or cardiovascular diseases). Childhood obesity is highly prevalent in Qatar and the Gulf Region. In this study we examine the feasibility of capturing quantified-self data from social media, wearables and mobiles within a weight lost camp for overweight children in Qatar.MethodsOver 50 children (9–12 years old) and parents used a wide range of technologies, including wearable sensors (actigraphy), mobile and social media (WhatsApp and Instagram) to collect data related to physical activity and food, that was then integrated with physiological data to gain insights about their health habits.In this paper, we report about the acquired data and visualization techniques following the 360° Quantified Self (360QS) methodology (Haddadi et al., ICHI 587–92, 2015).Results360QS allows for capturing insights on the behavioral patterns of children and serves as a mechanism to reinforce education of their mothers via social media. We also identified human factors, such as gender and cultural acceptability aspects that can affect the implementation of this technology beyond a feasibility study. Furthermore, technical challenges regarding the visualization and integration of heterogeneous and sparse data sets are described in the paper.ConclusionsWe proved the feasibility of using 360QS in childhood obesity through this pilot study. However, in order to fully implement the 360QS technology careful planning and integration in the health professionals’ workflow is needed.Trial RegistrationThe trial where this study took place is registered at ClinicalTrials.gov on 14 November 2016 (NCT02972164).

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Section: Discussionmentioning

confidence: 99%

Implementing 360° Quantified Self for childhood obesity: feasibility study and experiences from a weight loss camp in Qatar

Fernández-Luque

Singh

Ofli

et al. 2017

BMC Med Inform Decis Mak

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“…Jian-xunChen, Shih-LiSu and Che-Ha Chang discussed about Ontology that generate a primary care planning to the medical professionals. The result of the research paper shows the model can be provided personalize diabetes mellitus care planning efficiently [3]. MM Alotaib, RSH.Istepanian, and A.Sungoor they are present a clever based mobile polygenic disease control system & tutoring model for the patients with diabetes.…”

Section: Literature Reviewmentioning

confidence: 99%

Diabetes Risk Forecasting Using Logistic Regression

Metharani¹,

Srividya²,

Rekha

et al. 2021

Recent Trends in Intensive Computing

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Diabetes can be a collection of metabolic problems and lots of human beings are affected. Diabetes Mellitus can be caused by a variety of factors including age, stoopedness, lack of activity, inherited diabetes, lifestyle, poor eating habits, hypertension, and so on. Diabetics are more likely to develop diseases like coronary illness, kidney contamination, eye sickness, stroke and other risks. Distributed computing and Internet of Things (IoT) are two instruments that assume a vital part in the present life with respect to numerous angles and purposes including medical care observing of patients and old society. Diabetes Healthcare Monitoring Services are vital these days on the grounds that and that to distant medical care observing in light of the fact that truly going to clinics and remaining in a line is exceptionally ineffectual adaptation of patient checking. Current practice in emergency clinic is to gather required data for diabetes conclusion through different tests and proper treatment is given dependent on analysis. Utilizing enormous data investigation can consider large datasets and discover covered up data, uncertain examples to find information from the data and expect the outcome as demand. Diabetics are caused because of a tremendous uphill in the blood partition containing glucose. There is an advancement conspire accessible using train test split and K overlay cross approval utilizing Scikit learn technique. Various ML algorithms consisting of SVM, RF, KNN, NB, Decision Tree and Logistic Regression are also used.

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“…Despite this, it has a better connection at a twohour postprandial level. Alotaibi et al [8] developed a diabetes management system that sends SMS reminders to patients, as well as an interface for recording readings and an artificial intelligence unit that maintains the degree of health. Fuzzy logic Food intake monitoring is the technique of calculating or quantifying the number of calories consumed by examining a person's food intake throughout the day.…”

Section: Introductionmentioning

confidence: 99%

To Control Diabetes Using Machine Learning Algorithm and Calorie Measurement Technique

Viveka¹,

Columbus²,

Velmurugan³

2022

Intelligent Automation &Amp; Soft Computing

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Because of the increasing workload, people are having several clinical examinations to determine their health status, resulting in limited time. Here, we present a healthful consuming device based on rule mining that can modify your parameter dependency and recommend the varieties of meals that will boost your fitness and assist you to avoid the types of meals that increase your risk for sicknesses. Using the meals database, the data mining technique is useful for gathering meal energy from breakfast, after breakfast, lunch, after lunch, dinner, after dinner, and bedtime for ninety days. The purpose of this study is to determine to mean random plasma glucose levels and h1bc levels using the Nathan, ADAG (A1C-derived average glucose), and DTTC (Dynamic Temporal and Tactile Cueing) methods. This system can identify and recognize food images, as well as keep track of the food items ingested by the user. Deep learning techniques are mostly utilized for picture recognition and categorization. The KNN (k-nearest neighbors algorithm) classification approach is used to determine if diabetes is normal, pre-diabetic, or chronic. This study employs deep learning and a smart camera app called "calorie mom" to track nutrition from meal photographs. In addition, the commonly used measures of divisions such as accuracy, sensitivity, uniqueness, and recalling diabetic dataset using Python 3 Jupyter Notebook were employed to evaluate the performance of a machine learning classifier.

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An intelligent mobile diabetes management and educational system for Saudi Arabia: System architecture

Cited by 13 publications

References 3 publications

Implementing 360° Quantified Self for childhood obesity: feasibility study and experiences from a weight loss camp in Qatar

Implementing 360° Quantified Self for childhood obesity: feasibility study and experiences from a weight loss camp in Qatar

Diabetes Risk Forecasting Using Logistic Regression

To Control Diabetes Using Machine Learning Algorithm and Calorie Measurement Technique

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