Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)

Abdollahi, Jafar; Nouri-Moghaddam, Babak; Ghazanfari, Mehdi

doi:10.48550/arxiv.2103.08182

Cited by 8 publications

(8 citation statements)

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“…Through machine learning and deep learning technology, we can learn from large amounts of data and discover patterns and laws in the system, helping to understand and solve complex problems in biological systems, social systems and other fields. 1.Prediction and classification: In complex systems, machine learning and deep learning can be used for prediction and classification [57]. By learning and discovering patterns and regularities from large amounts of data, prediction models and classification models can be built to predict future trends and classify new technologies.…”

Section: Deep Learning and Machine Learning In Mathematical Modelling...mentioning

confidence: 99%

Artificial Intelligence in Mathematical Modeling of Complex Systems

Zhao

2024

EAI Endorsed Trans e-Learn

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This article introduces artificial intelligence techniques in mathematical modelling of complex systems and their applications. Mathematical modelling of complex systems is a method of studying the structure and behaviour of complex systems, aiming to understand interactions and nonlinear effects in the system. Commonly used modelling methods include system dynamics, network theory, and algebraic methods. Artificial intelligence technologies include machine learning and deep learning, which can be used for tasks such as prediction and classification, anomaly detection, optimization and decision-making. In mathematical modelling of complex systems, artificial intelligence technology can learn system patterns and laws from large amounts of data, and can be applied to image and speech recognition, time series analysis and other fields. Deep learning and machine learning are important branches of artificial intelligence. They realize the modelling and analysis of complex systems by building neural network models. Data-driven modelling is a modelling method based on actual data that, combined with traditional theoretical modelling, can better describe and predict the behaviour of complex systems. Self-control of complex systems means that the system realizes its own optimization and adjustment through adaptive control algorithms and feedback mechanisms. In summary, artificial intelligence technology has broad application prospects in mathematical modelling of complex systems and will provide new tools and methods for in-depth understanding and solving problems in complex systems.

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Section: Deep Learning and Machine Learning In Mathematical Modelling...mentioning

confidence: 99%

Artificial Intelligence in Mathematical Modeling of Complex Systems

Zhao

2024

EAI Endorsed Trans e-Learn

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“…In the confusion matrix effectively communicates the algorithm's classification performance, offering a granular insight into its ability to correctly identify materials with and without the desired properties. The discussion underscores the significance of the matrix entries, emphasizing areas for improvement and providing actionable insights for refining the algorithm's precision in material selection for wearable medical devices (Abdollahi, J., et al, 2021).…”

Section: Confusion Matrixmentioning

confidence: 99%

Algorithmic material selection for wearable medical devices a genetic algorithm-based framework with multiscale modeling

Wange,

Chaudhari,

Seelam

et al. 2024

The Scientific Temper

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This research presents a novel algorithmic material selection framework for wearable medical devices, utilizing a genetic algorithm-based approach with multiscale modeling. The study employs a comprehensive research methodology encompassing computational modeling, data visualization, and performance assessment. Initially, a diverse set of materials is defined, and their performance scores are assigned to establish a baseline for evaluation. The ensuing data visualization includes a bar chart, a scatter plot, and a line chart, providing insights into material performance, cost-performance relationships, and the convergence of the genetic algorithm, respectively. Performance metrics such as accuracy, precision, and recall are calculated to gauge the algorithm’s efficacy, presented in a bar chart for a nuanced evaluation. Furthermore, a receiver operating characteristic (ROC) curve and a confusion matrix are employed for discriminative ability assessment and detailed classification performance analysis. The results showcase the algorithm’s proficiency in material selection, emphasizing the importance of accuracy, precision, and recall in the complex landscape of wearable medical device development. The abstract concludes with a summary of the implications drawn from each visualization, highlighting the potential of the proposed algorithmic framework to enhance the precision and efficiency of material selection processes for wearable medical devices. This research contributes to the advancement of materials science in healthcare applications, presenting a holistic approach that integrates computational techniques and data-driven methodologies for optimized material selection

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“…Moreover, we knew the correctness of the binary model of the F1 score. To determine the comparability and variability of efficiencies, efficiencies were further calculated using F-measures (F1) [7,8].…”

Section: Evaluation Criteriamentioning

confidence: 99%

Diabetes Data Classification using Deep Learning Approach and Feature Selection based on Genetic

Abdollahi

Nouri-Moghaddam

Mirzaei

2023

Preprint

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The area of deep learning research (DL) has seen significant growth in recent years and has shown incredible promise for AI in medical applications. In this approach, we have proposed a deep neural network framework for the categorization of feature-based diabetes data. The dataset is classified using the SoftMax layer after features are collected from it using the wrapper approach. Additionally, the network is adjusted through supervised grid research with the training dataset. However, due to the risk of misdiagnosis in a medical context, we evaluated our model using measures such as precision, recall, specificity, and F1 score and obtained superior results. The proposed architecture has been tested using Diabetes 130-US hospital data, which consists of 100,000 patient files with an average of 55 attributes. As well, five machine learning algorithms—Nave Bayes, Random Forests, Decision Trees, Support vector machines, and Ensemble learning—are used to compare results from experiments. The classification accuracy of the ML algorithm was 86%. This research suggests a Deep Learning system based on feature selection for classifying diabetes data. This method has been tested on UCI machine learning data and has outperformed several other classification techniques, reaching 85.61% accuracy in this study.

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Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)

Cited by 8 publications

References 0 publications

Artificial Intelligence in Mathematical Modeling of Complex Systems

Artificial Intelligence in Mathematical Modeling of Complex Systems

Algorithmic material selection for wearable medical devices a genetic algorithm-based framework with multiscale modeling

Diabetes Data Classification using Deep Learning Approach and Feature Selection based on Genetic

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