Diabetes Predicting mHealth Application Using Machine Learning

Khan, Nabila Shahnaz; Muaz, Mehedi Hasan; Kabir, Anusha; Islam, Muhammad Nazrul

doi:10.1109/wiecon-ece.2017.8468885

Cited by 27 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These algorithms can then be implemented in a mobile intervention to deliver intervention prompts at the predicted times. Such algorithms are already used in the HeartSteps program and other mHealth interventions (Khan, 2018;Liao et al, 2018Liao et al, , 2020Tsang, 2020). It is important to note that explanatory and predictive modelling approaches do not have to be separate research areas, but are, in contrast, best applied in tandem in the model development process to produce interpretable and actionable results (Rosé et al, 2019).…”

Section: Taking Into Account the Impact Of Context On The Effectivene...mentioning

confidence: 99%

Mobile Interventions: An Untapped Potential for Education

Breitwieser¹,

Neubauer²,

Schmiedek³

et al. 2023

Preprint

View full text Add to dashboard Cite

Mobile devices like smartphones and smartwatches are ubiquitous both inside and outside the classroom, but their potential for personalized education has yet to be realized. The data collected by wearable devices can be used to provide the right type and amount of support to a student at the right time. In the present work, we argue that adaptive mobile interventions can improve students’ learning and well-being and at the same time advance researchers’ understanding of educational processes. Therefore, mobile interventions which have thus far primarily attracted attention in mobile health research, provide tremendous potential for applications in educational research and practice. We identify three key promises of mobile interventions: 1) being able to intervene when and where it is most beneficial (i.e., “just-in-time adaptivity”), 2) estimating causal effects of interventions in ecologically valid settings, 3) taking into account the impact of context on the effectiveness of interventions. We draw on initial examples of mobile interventions from educational contexts, as well as a broader literature on mobile health interventions, to illustrate these promises. We conclude with a discussion of open questions and next steps for establishing mobile intervention research in education.

show abstract

Section: Taking Into Account the Impact Of Context On The Effectivene...mentioning

confidence: 99%

Mobile Interventions: An Untapped Potential for Education

Breitwieser¹,

Neubauer²,

Schmiedek³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…With the massive expansion of data in healthcare sector, ML is widely employed to analyze electronic health records (EHR) or patient data and create effective clinical decision support systems for different illness diagnosis or forecasting [19] , [20] . ML techniques are applied to detect various kinds of critical diseases autonomously such as, cardiac anomalies [21] , mode of childbirth [22] , [23] , diabetes detection [24] , [25] , Alzheimer's disease diagnosis [26] etc.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A systematic review and future research agenda on detection of polycystic ovary syndrome (PCOS) with computer-aided techniques

Suha,

Islam

2023

Heliyon

View full text Add to dashboard Cite

“…The paper [19] aimed to implement an intelligent mHealth application based on an ML mechanism to predict diabetes and classify it into three categories: diabetes, prediabetes, and non-diabetes. The paper [20] had reflected on the execution of a scheme to analyze diabetes infection utilizing different types of ML algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Dm-Health App: Diabetes Diagnosis Using Machine Learning with Smartphone

Hossain¹,

Alshehri²,

Almakdi³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Diabetes Mellitus is one of the most severe diseases, and many studies have been conducted to anticipate diabetes. This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic, pre-diabetic, or non-diabetic without the assistance of any physician or medical tests. This study's methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design. The Diabetes Prediction Approach uses a novel approach, Light Gradient Boosting Machine (LightGBM), to ensure a faster diagnosis. The Proposed System Architecture Design has been combined into seven modules; the Answering Question Module is a natural language processing Chabot that can answer all kinds of questions related to diabetes. The Doctor Consultation Module ensures free treatment related to diabetes. In this research, 90% accuracy was obtained by performing K-fold cross-validation on top of the K nearest neighbor's algorithm (KNN) & LightGBM. To evaluate the model's performance, Receiver Operating Characteristics (ROC) Curve and Area under the ROC Curve (AUC) were applied with a value of 0.948 and 0.936, respectively. This manuscript presents some exploratory data analysis, including a correlation matrix and survey report. Moreover, the proposed solution can be adjustable in the daily activities of a diabetic patient.

show abstract

Diabetes Predicting mHealth Application Using Machine Learning

Cited by 27 publications

References 13 publications

Mobile Interventions: An Untapped Potential for Education

Mobile Interventions: An Untapped Potential for Education

A systematic review and future research agenda on detection of polycystic ovary syndrome (PCOS) with computer-aided techniques

Dm-Health App: Diabetes Diagnosis Using Machine Learning with Smartphone

Contact Info

Product

Resources

About