Multi-modal Predictive Models of Diabetes Progression

Ramazi, Ramin; Perndorfer, Christine; Soriano, Emily C; Laurenceau, Jean-Philippe; Beheshti, Rahmatollah

doi:10.1145/3307339.3342177

Cited by 14 publications

(10 citation statements)

References 14 publications

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“…Soniya et al [33] proposed joining a hybrid evolutionary approach with a convolutional neural network (CNN) and determined the number of layers and filters based on the application and user needs. Ramazi et al [34] developed a wide and deep neural network and used the data from demographic information, lab tests, and wearable sensors to create the model. Alharbi et al [35] proposed a hybrid algorithm, the GA-ELM algorithm, which optimally diagnosed type 2-diabetes patients, and classified the data set with an accuracy of 97.5% using six effective features out of the original eight features given in the dataset.…”

Section: Related Workmentioning

confidence: 99%

Diabetes prediction model based on an enhanced deep neural network

Zhou

Myrzashova

Zheng

2020

J Wireless Com Network

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Today, diabetes is one of the most common, chronic, and, due to some complications, deadliest diseases in the world. The early detection of diabetes is very important for its timely treatment since it can stop the progression of the disease. The proposed method can help not only to predict the occurrence of diabetes in the future but also to determine the type of the disease that a person experiences. Considering that type 1 diabetes and type 2 diabetes have many differences in their treatment methods, this method will help to provide the right treatment for the patient. By transforming the task into a classification problem, our model is mainly built using the hidden layers of a deep neural network and uses dropout regularization to prevent overfitting. We tuned a number of parameters and used the binary cross-entropy loss function, which obtained a deep neural network prediction model with high accuracy. The experimental results show the effectiveness and adequacy of the proposed DLPD (Deep Learning for Predicting Diabetes) model. The best training accuracy of the diabetes type data set is 94.02174%, and the training accuracy of the Pima Indians diabetes data set is 99.4112%. Extensive experiments have been conducted on the Pima Indians diabetes and diabetic type datasets. The experimental results show the improvements of our proposed model over the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Diabetes prediction model based on an enhanced deep neural network

Zhou

Myrzashova

Zheng

2020

J Wireless Com Network

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“…We included articles that described or reported research focused on measures of physical activity of VIP, monitoring or improving QoL activities, and systems developed to aid athletes with visual impairments. Research that focuses on monitoring physiological and behavioral patterns with wearable devices, such as inertial sensors, has become more prevalent due to the increasing availability of wearable small devices [ 113 ]. Wearable accelerometers are widely used in adapted PA research, since physical inactivity is a serious health issue in VIP [ 61 ].…”

Section: Discussionmentioning

confidence: 99%

Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Leiva

Jaén-Vargas

Codina

et al. 2021

Sensors

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A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

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“…С увеличением доступности носимых устройств стал значительно более доступным непрерывный мониторинг физиологических и поведенческих особенностей людей. Работа [8] интересна еще и тем, что в модели использовались данные временных рядов из непрерывных измерений глюкозы и активности, а затем они объединялись с независящими от времени демографическими данными аналогично тому, как это делается в передовой системе [5]. Однако в этой работе использовалась долгая краткосрочная память (Long Short-Term Memory; LSTM), обеспечивающая хорошую производительность при работе с данными временных рядов и значительную общую точность.…”

Section: обзор литературыunclassified

“…Как сообщается в [5], эта система обеспечивают наилучшие точность и AUC ROC при решении задачи прогнозирования СД2. Использованию ансамблевой модели для выявления сахарного диабета посвящена и впечатляющая работа [8], которую можно считать развитием [5] из-за разнообразия используемых моделей. Однако по своим результатам [8] не опережает [5].…”

Section: предлагаемая системаunclassified

“…Использованию ансамблевой модели для выявления сахарного диабета посвящена и впечатляющая работа [8], которую можно считать развитием [5] из-за разнообразия используемых моделей. Однако по своим результатам [8] не опережает [5]. Одна из основных…”

Section: предлагаемая системаunclassified

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A Novel Intelligent System for Detection of Type 2 Diabetes with Modified Loss Function and Regularization

Маллика¹,

Альсадун²,

Фам³

et al. 2021

Proceedings of ISP RAS

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Type 2 Diabetes (T2DM) makes up about 90% of diabetes cases, as well as tough restriction on continuous monitoring and detecting become one of key aspects in T2DM. This research aims to develop an ensemble of several machine learning and deep learning models for early detection of T2DM with high accuracy. With high diversity of models, the ensemble will provide more excessive performance than single models. Methodology: The proposed system is modified enhanced ensemble of machine learning models for T2DM prediction. It is composed of Logistic Regression, Random Forest, SVM and Deep Neural Network models to generate a modified ensemble model. Results: The output of each model in the modified ensemble is used to figure out the final output of the system. The datasets being used for these models include Practice Fusion HER, Pima Indians diabetic's data, UCI AIM94 Dataset and CA Diabetes Prevalence 2014. In comparison to the previous solutions, the proposed ensemble model solution exposes the effectiveness of accuracy, sensitivity, and specificity. It provides an accuracy of 87.5% from 83.51% in average, sensitivity of 35.8% from 29.59% as well as specificity of 98.9% from 96.27%. The processing time of the proposed model solution with 96.6ms is faster than the state-of-the-art with 97.5ms. Conclusion: The proposed modified enhanced system in this work improves the overall prediction capability of T2DM using an ensemble of several machine learning and deep learning models. A majority voting scheme utilizes the output from several models to make the final accurate prediction. Regularization function in this work is modified in order to include the regularization of all the models in ensemble, that helps prevent the overfitting and encourages the generalization capacity of the proposed system.

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Multi-modal Predictive Models of Diabetes Progression

Cited by 14 publications

References 14 publications

Diabetes prediction model based on an enhanced deep neural network

Diabetes prediction model based on an enhanced deep neural network

Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

A Novel Intelligent System for Detection of Type 2 Diabetes with Modified Loss Function and Regularization

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