Deep Learning for Blood Glucose Prediction: CNN vs LSTM

Idrissi, Touria El; Idri, Ali

doi:10.1007/978-3-030-58802-1_28

Cited by 15 publications

(5 citation statements)

References 18 publications

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“…This discussion aims to contextualize the results of the 7-layer LSTM model developed in our study within the broader landscape of LSTM applications in diabetes prediction. The Conv-LSTM model [4], which the Pima Indians Diabetes Database [30], achieved an impressive accuracy of 97.26%, although specific metrics like precision, recall, and sensitivity were not disclosed. This indicates a strong baseline performance for LSTM models in diabetes prediction.…”

Section: Discussionmentioning

confidence: 99%

Leveraging a 7-Layer Long Short-Term Memory Model for Early Detection and Prevention of Diabetes in Oman: An Innovative Approach

Al Sadi,

Balachandran

2024

Bioengineering

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This study develops a 7-layer Long Short-Term Memory (LSTM) model to enhance early diabetes detection in Oman, aligning with the theme of ‘Artificial Intelligence in Healthcare’. The model focuses on addressing the increasing prevalence of Type 2 diabetes, projected to impact 23.8% of Oman’s population by 2050. It employs LSTM neural networks to manage factors contributing to this rise, including obesity and genetic predispositions, and aims to bridge the gap in public health awareness and prevention. The model’s performance is evaluated through various metrics. It achieves an accuracy of 99.40%, specificity and sensitivity of 100% for positive cases, a recall of 99.34% for negative cases, an F1 score of 96.24%, and an AUC score of 94.51%. These metrics indicate the model’s capability in diabetes detection. The implementation of this LSTM model in Oman’s healthcare system is proposed to enhance early detection and prevention of diabetes. This approach reflects an application of AI in addressing a significant health concern, with potential implications for similar healthcare challenges relating to globally diagnostic capabilities, representing a significant leap forward in healthcare technology in Oman.

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Section: Discussionmentioning

confidence: 99%

Leveraging a 7-Layer Long Short-Term Memory Model for Early Detection and Prevention of Diabetes in Oman: An Innovative Approach

Al Sadi,

Balachandran

2024

Bioengineering

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“…Measures glucose levels over time Predicting blood glucose levels [146,147] Pulse oximeter (Time series)…”

Section: Measures Electrical Signals Of the Heart Over Timementioning

confidence: 99%

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Chato,

Regentova

2023

JPM

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Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

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“…CNN has been utilized in many applications, e.g., heart-beat classification ( Acharya et al, 2017 ), COVID-19 detection ( Wang et al, 2020 ), and glaucoma detection ( Chen et al, 2015 ). Models based on CNN have also been developed to predict and forecast blood glucose levels and trends ( Swapna et al, 2018 ; Idrissi and Idri, 2020 ; Kamalraj et al, 2021 ). A personalized CNN model employing a fine-tuning strategy improved the prediction horizon performance compared to standard CNN when evaluated using CGM data ( Seo et al, 2021 ).…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection

Alhaddad

Aly

Gad

et al. 2022

Front. Bioeng. Biotechnol.

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Diabetes mellitus is characterized by elevated blood glucose levels, however patients with diabetes may also develop hypoglycemia due to treatment. There is an increasing demand for non-invasive blood glucose monitoring and trends detection amongst people with diabetes and healthy individuals, especially athletes. Wearable devices and non-invasive sensors for blood glucose monitoring have witnessed considerable advances. This review is an update on recent contributions utilizing novel sensing technologies over the past five years which include electrocardiogram, electromagnetic, bioimpedance, photoplethysmography, and acceleration measures as well as bodily fluid glucose sensors to monitor glucose and trend detection. We also review methods that use machine learning algorithms to predict blood glucose trends, especially for high risk events such as hypoglycemia. Convolutional and recurrent neural networks, support vector machines, and decision trees are examples of such machine learning algorithms. Finally, we address the key limitations and challenges of these studies and provide recommendations for future work.

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Deep Learning for Blood Glucose Prediction: CNN vs LSTM

Cited by 15 publications

References 18 publications

Leveraging a 7-Layer Long Short-Term Memory Model for Early Detection and Prevention of Diabetes in Oman: An Innovative Approach

Leveraging a 7-Layer Long Short-Term Memory Model for Early Detection and Prevention of Diabetes in Oman: An Innovative Approach

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection

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