D-CARE: A Non-invasive Glucose Measuring Technique for Monitoring Diabetes Patients

Alam, M. M.; Saha, Swapnil; Saha, Proshib; Nur, Fernaz Narin; Moon, Nazmun Nessa; Karim, Asif; Azam, Sami

doi:10.1007/978-981-13-7564-4_38

Cited by 15 publications

(6 citation statements)

References 12 publications

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“…Various risk variables and features have been incorporated into the models of CVD predictors by various studies. Features employed in the construction of CVD prediction models include sex, age, and chest pain (cp); higher FBS is associated with diabetes [15] [16].…”

Section: Introductionmentioning

confidence: 99%

Machine intelligence based early prediction methods for Cardiovascular Diseases (CVDs)

Sneha Kanawade, Priyadarshani Doke, Deepali Hajare

2024

jes

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Exploring early indications of heart disease is challenging in today's society. It may lead to death if not detected early. Early identification of cardiac disease in remote, rural, and semi-urban locations in developing nations can be greatly aided by an accurate decision support system (DSS). To assist in heart-related disease detection at an early stage, this system offers a DSS which uses artificial learning techniques using the patient's clinical details. A hybridized class balancing methodology using the chi2 feature selection algorithm, random oversampling, and undersampling techniques were used to find the suitable features from the presented dataset. Standard scalar approaches have also been utilized for data preprocessing. In the last stage of developing the suggested Artificial Intelligence (AI) system, the system used support vector machines (SVM), naive Bayes, random forest classifiers, logistic regression, and improved Artificial Neural Network (ANN) classifiers. Tests were conducted on the Python-based simulation environment to evaluate the proposed system. Evaluation of the system is completed using the Cleveland heart disease dataset by the machine repository at UCI. A 96.74% accuracy rate was reached, which is better than some previously published models for heart-related disease prediction.

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Section: Introductionmentioning

confidence: 99%

Machine intelligence based early prediction methods for Cardiovascular Diseases (CVDs)

Sneha Kanawade, Priyadarshani Doke, Deepali Hajare

2024

jes

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“…Various attributes or features, such as sex, age, fasting blood sugar, chest pain [5], chest pain type, chest pain location, blood sugar level, cigarette habit, depression level, electrocardiogram [6], exercise-induced angina (exang), resting electrocardiographic results, slope, old peak, heart status, poor diet, cholesterol, obesity, family history, alcohol intake, high blood pressure, and physical inactivity [7][8][9][10][11][12][13], have been used in different research studies.…”

Section: Introductionmentioning

confidence: 99%

An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction

Revathi,

Balasubramaniam,

Sureshkumar

et al. 2024

Diagnostics

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Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured and Improved Long Short-Term Memory (OCI-LSTM) model as a robust solution. Leveraging the Salp Swarm Algorithm, irrelevant features are systematically eliminated, and the Genetic Algorithm is employed to optimize the LSTM’s network configuration. Validation metrics, including the accuracy, sensitivity, specificity, and F1 score, affirm the model’s efficacy. Comparative analysis with a Deep Neural Network and Deep Belief Network establishes the OCI-LSTM’s superiority, showcasing a notable accuracy increase of 97.11%. These advancements position the OCI-LSTM as a promising model for accurate and efficient early diagnosis of cardiovascular diseases. Future research could explore real-world implementation and further refinement for seamless integration into clinical practice.

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“…Glucose sensing has a significant scientific, clinical, and industrial relevance and significant progress has been made recently, particularly related to non-invasive methods to monitor blood [ 42 , 43 , 44 , 45 ]. Typically, glucose sensor reviews focus on a specific approach, enzymatic or non-enzymatic, in this review we discuss and compare both techniques and it is aimed at experts or new researchers in the field.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Enzymatic and Non-Enzymatic Electrochemical Glucose Sensing

Hassan

Vyas

Grieve

et al. 2021

Sensors

182

111

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The detection of glucose is crucial in the management of diabetes and other medical conditions but also crucial in a wide range of industries such as food and beverages. The development of glucose sensors in the past century has allowed diabetic patients to effectively manage their disease and has saved lives. First-generation glucose sensors have considerable limitations in sensitivity and selectivity which has spurred the development of more advanced approaches for both the medical and industrial sectors. The wide range of application areas has resulted in a range of materials and fabrication techniques to produce novel glucose sensors that have higher sensitivity and selectivity, lower cost, and are simpler to use. A major focus has been on the development of enzymatic electrochemical sensors, typically using glucose oxidase. However, non-enzymatic approaches using direct electrochemistry of glucose on noble metals are now a viable approach in glucose biosensor design. This review discusses the mechanisms of electrochemical glucose sensing with a focus on the different generations of enzymatic-based sensors, their recent advances, and provides an overview of the next generation of non-enzymatic sensors. Advancements in manufacturing techniques and materials are key in propelling the field of glucose sensing, however, significant limitations remain which are highlighted in this review and requires addressing to obtain a more stable, sensitive, selective, cost efficient, and real-time glucose sensor.

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D-CARE: A Non-invasive Glucose Measuring Technique for Monitoring Diabetes Patients

Cited by 15 publications

References 12 publications

Machine intelligence based early prediction methods for Cardiovascular Diseases (CVDs)

Machine intelligence based early prediction methods for Cardiovascular Diseases (CVDs)

An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction

Recent Advances in Enzymatic and Non-Enzymatic Electrochemical Glucose Sensing

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