Software Design Specification and Analysis of Insulin Dose to Adaptive Carbohydrate Algorithm for Type 1 Diabetic Patients

Gambo, Ishaya; Massenon, Rhodes; Yange, Terungwa Simon; Ikono, Rhoda; Omodunbi, Theresa; Babatope, Kolawole

doi:10.1007/978-981-15-9735-0_7

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…Additionally, the internal validity threat related to result acceptance and confidence was mitigated by demonstrating the implemented tool to the experts, showcasing the technological and scientific processes involved. External validity, the second threat, underscores the need for validation with real-life datasets in the problem domain and adherence to software design principles for future LBP prediction and classification systems, as emphasized in [37], [38].…”

Section: Threats To Validitymentioning

confidence: 99%

Implementing decision support tool for low-back pain diagnosis and prediction based on the range of motions

Gambo,

Mbada,

Aina

et al. 2024

IJEECS

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Low-back pain (LBP) is a complex health problem requiring accurate diagnosis and effective treatment. However, the current decision support system (DSS) for LBP only considers the patient’s pain intensity and treatment suitability, which may not lead to optimal outcomes. This paper proposes a novel DSS that combines machine learning (ML) and expert input to classify LBP types and provide more reliable and personalized recommendations. We used an open-source dataset to train and test various ML models, including an ensemble model that combines multiple classifiers. We also performed data analysis and feature extraction to enhance the model’s predictive power. We developed a prototype tool to demonstrate the model’s performance and usability. Our results show that the ensemble model achieved the highest accuracy of 92.02%, followed by random forest (RF) (91.01%), multilayer perceptron (MP) (91.01%), and support vector machine (SVM) (87.88%). Our findings suggest that ML can help LBP specialists diagnose and treat LBP more effectively by learning from historical data and predicting LBP categories. Our DSS can potentially improve the quality of life for LBP patients and reduce the burden on the healthcare system.

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Section: Threats To Validitymentioning

confidence: 99%

Implementing decision support tool for low-back pain diagnosis and prediction based on the range of motions

Gambo,

Mbada,

Aina

et al. 2024

IJEECS

View full text Add to dashboard Cite

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“…Remarkably, diabetes requires regular selfmonitoring; and in cases of complications, it can have a significant impact on a patient's health and even diminish life expectancy (American Diabetes Association, 2014). As Gambo et al (2021) observed, the control of diabetes mellitus is experienced by several means. Therefore, T1DM requires lifetime treatment with exogenous insulin, which exposes patients to many problems (Soylu et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Analysis and Design Process for Predicting and Controlling Blood Glucose in Type 1 Diabetic Patients

Gambo

Massenon

Kolawole

et al. 2021

International Journal of Healthcare Information Systems and Informatics

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Engineering smart software that can monitor, predict, and control blood glucose is critical to improving patients' quality of treatments with type 1 Diabetic Mellitus (T1DM). However, ensuring a reasonable glycemic level in diabetic patients is quite challenging, as many methods do not adequately capture the complexities involved in glycemic control. This problem introduces a new level of complexity and uncertainty to the patient's psychological state, thereby making this problem nonlinear and unobservable. In this paper, we formulated a mathematical model using carbohydrate counting, insulin requirements, and the Harris-Benedict energy equations to establish the framework for predicting and controlling blood glucose level regulation in T1DM. We implemented the framework and evaluated its performance using root mean square error (RMSE) and mean absolute error (MAE) on a case study. Our framework had less error rate in terms of RMSE and MAE, which indicates a better fit with reasonable accuracy.

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“…From a more technical perspective, the inability to meet older adults' requirements, including emotional requirements (12), in the design of the technological solutions could endanger human lives (13), which eventually also leads to a low level of adoption. Remarkably, several researchers have pointed out that if the needs of older adults are not adequately captured in the analysis and design process, the system's functionality will not be trusted and reliable (14)(15)(16).…”

mentioning

confidence: 99%

Technology adoption review for ageing well: analysis of technical solutions

Gambo,

Bueno-Delgado,

Mooses

et al. 2023

Front. Public Health

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While several technological solutions are available for older adults to improve their wellbeing and quality of life, little is known about the gaps between the needs, provided solutions, and their adoption from a more pragmatic perspective. This paper reports on reviewing existing technological solutions for older adults, which span the work life, life in the community, and wellbeing at home. We analyzed 50 different solutions to uncover both negative and positive features of these solutions from the perspective of the impact of technology adoption on the quality of life of older adults. Our approach harnesses holistic reasoning to determine the most suitable technologies available today and provides suggestions for improvement toward designing and implementing better solutions.

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Software Design Specification and Analysis of Insulin Dose to Adaptive Carbohydrate Algorithm for Type 1 Diabetic Patients

Cited by 4 publications

References 38 publications

Implementing decision support tool for low-back pain diagnosis and prediction based on the range of motions

Implementing decision support tool for low-back pain diagnosis and prediction based on the range of motions

Analysis and Design Process for Predicting and Controlling Blood Glucose in Type 1 Diabetic Patients

Technology adoption review for ageing well: analysis of technical solutions

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