Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

Ismail, Leila; Materwala, Huned; Tayefi, Maryam; Ngo, Phuong Thao Thi; Karduck, Achim P.

doi:10.1007/s11831-021-09582-x

Cited by 36 publications

(25 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tripathi and Kumar [55] used random oversampling (ROS), normalization, and several classifiers like Linear Discriminant Analysis (LDA), KNN, SVM, and RF were used to investigate their primary diabetes dataset for machine learning purposes. Ismail et al [22] provided a taxonomy of significant factors where different machine learning algorithms were used with or without feature selection processing. In addition, Ramesh et al [44] implemented multivariate imputation by chained equations (MICE) method for handling missing values of primary diabetes dataset.…”

Section: Related Workmentioning

confidence: 99%

Machine learning models for classification and identification of significant attributes to detect type 2 diabetes

Howlader

Satu

Awal

et al. 2022

Health Inf Sci Syst

View full text Add to dashboard Cite

Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analyze these. We then compared the derived classification results to identify the best classifiers by considering accuracy, kappa statistics, area under the receiver operating characteristic (AUROC), sensitivity, specificity, and logarithmic loss (logloss). To evaluate the performance of different classifiers, we investigated their outcomes using the summary statistics with a resampling distribution. Therefore, Generalized Boosted Regression modeling showed the highest accuracy (90.91%), followed by kappa statistics (78.77%) and specificity (85.19%). In addition, Sparse Distance Weighted Discrimination, Generalized Additive Model using LOESS and Boosted Generalized Additive Models also gave the maximum sensitivity (100%), highest AUROC (95.26%) and lowest logarithmic loss (30.98%) respectively. Notably, the Generalized Additive Model using LOESS was the top-ranked algorithm according to non-parametric Friedman testing. Of the features identified by these machine learning models, glucose levels, body mass index, diabetes pedigree function, and age were consistently identified as the best and most frequently accurate outcome predictors. These results indicate the utility of ML methods in constructing improved prediction models for T2D and successfully identified outcome predictors for this Pima Indian population.

show abstract

Section: Related Workmentioning

confidence: 99%

Machine learning models for classification and identification of significant attributes to detect type 2 diabetes

Howlader

Satu

Awal

et al. 2022

Health Inf Sci Syst

View full text Add to dashboard Cite

show abstract

“…It is based on a pay-per-use model and can be provisioned with minimal management effort. With the emergence of the IoT and big data analytics applications in various domains such as healthcare [ 125 , 126 , 127 ], education [ 128 , 129 , 130 ], transportation [ 131 ], banking [ 132 , 133 ], energy utilities [ 134 , 135 ], and entertainment [ 136 , 137 ], cloud computing provides a sandbox for data processing and storage, enabling the deployment of compute-intensive smart city applications [ 138 ]. However, considering the distance between the IoT devices and the remote cloud servers, the latency requirements of time-critical applications may be violated.…”

Section: Taxonomy Of Technology-enabled Smart City Applications In 6g...mentioning

confidence: 99%

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Ismail

Buyya

2022

Sensors

Self Cite

View full text Add to dashboard Cite

The recent upsurge of smart cities’ applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics, blockchain, and edge-cloud computing has urged the design of the upcoming 6G network generation, due to their stringent requirements in terms of the quality of services (QoS), availability, and dependability to satisfy a Service-Level-Agreement (SLA) for the end users. Industries and academia have started to design 6G networks and propose the use of AI in its protocols and operations. Published papers on the topic discuss either the requirements of applications via a top-down approach or the network requirements in terms of agility, performance, and energy saving using a down-top perspective. In contrast, this paper adopts a holistic outlook, considering the applications, the middleware, the underlying technologies, and the 6G network systems towards an intelligent and integrated computing, communication, coordination, and decision-making ecosystem. In particular, we discuss the temporal evolution of the wireless network generations’ development to capture the applications, middleware, and technological requirements that led to the development of the network generation systems from 1G to AI-enabled 6G and its employed self-learning models. We provide a taxonomy of the technology-enabled smart city applications’ systems and present insights into those systems for the realization of a trustworthy and efficient smart city ecosystem. We propose future research directions in 6G networks for smart city applications.

show abstract

“…AI systems can emerge as a smart solution to reduce clinical staff workload in a world with increasingly saturated healthcare systems. AI is different from simple technology interventions in the sense that AI does not just manage data, but it provides suggestions and recommendations directly shaping the clinical decision process [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

Chomutare

Tejedor

Svenning

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

There is a large proliferation of complex data-driven artificial intelligence (AI) applications in many aspects of our daily lives, but their implementation in healthcare is still limited. This scoping review takes a theoretical approach to examine the barriers and facilitators based on empirical data from existing implementations. We searched the major databases of relevant scientific publications for articles related to AI in clinical settings, published between 2015 and 2021. Based on the theoretical constructs of the Consolidated Framework for Implementation Research (CFIR), we used a deductive, followed by an inductive, approach to extract facilitators and barriers. After screening 2784 studies, 19 studies were included in this review. Most of the cited facilitators were related to engagement with and management of the implementation process, while the most cited barriers dealt with the intervention’s generalizability and interoperability with existing systems, as well as the inner settings’ data quality and availability. We noted per-study imbalances related to the reporting of the theoretic domains. Our findings suggest a greater need for implementation science expertise in AI implementation projects, to improve both the implementation process and the quality of scientific reporting.

show abstract

Type 2 Diabetes with Artificial Intelligence Machine Learning: Methods and Evaluation

Cited by 36 publications

References 54 publications

Machine learning models for classification and identification of significant attributes to detect type 2 diabetes

Machine learning models for classification and identification of significant attributes to detect type 2 diabetes

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators

Contact Info

Product

Resources

About