An Improved Artificial Neural Network Model for Effective Diabetes Prediction

Bukhari, Muhammad Mazhar; Alkhamees, Bader Fahad; Hussain, Saddam; Gumaei, Abdu; Assiri, Adel; Ullah, Syed Sajid

doi:10.1155/2021/5525271

Cited by 83 publications

(33 citation statements)

References 14 publications

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“…The objective of ML applications is to train the computer system to perform better than a human being. The supervised learning algorithm is used for training the model, and evaluation is done using testing data [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques

Krishnamoorthi¹,

Joshi

Almarzouki

et al. 2022

Journal of Healthcare Engineering

166

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Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population’s health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study’s primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.

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Section: Introductionmentioning

confidence: 99%

A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques

Krishnamoorthi¹,

Joshi

Almarzouki

et al. 2022

Journal of Healthcare Engineering

166

View full text Add to dashboard Cite

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“…Image features can be consistently extracted using the convolutional neural network (CNN) [ 19 ], which is a deep learning architecture. It has been widely used both in academic circles and in actual business applications, especially in the field of computer vision [ 20 – 27 ].…”

Section: Introductionmentioning

confidence: 99%

Impact of Activation, Optimization, and Regularization Methods on the Facial Expression Model Using CNN

Kandhro

Uddin

Hussain

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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When it comes to conveying sentiments and thoughts, facial expressions are quite effective. For human-computer collaboration, data-driven animation, and communication between humans and robots to be successful, the capacity to recognize emotional states in facial expressions must be developed and implemented. Recently published studies have found that deep learning is becoming increasingly popular in the field of image categorization. As a result, to resolve the problem of facial expression recognition (FER) using convolutional neural networks (CNN), increasingly substantial efforts have been made in recent years. Facial expressions may be acquired from databases like CK+ and JAFFE using this novel FER technique based on activations, optimizations, and regularization parameters. The model recognized emotions such as happiness, sadness, surprise, fear, anger, disgust, and neutrality. The performance of the model was evaluated using a variety of methodologies, including activation, optimization, and regularization, as well as other hyperparameters, as detailed in this study. In experiments, the FER technique may be used to recognize emotions with an Adam, Softmax, and Dropout Ratio of 0.1 to 0.2 when combined with other techniques. It also outperforms current FER techniques that rely on handcrafted features and only one channel, as well as has superior network performance compared to the present state-of-the-art techniques.

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“…The traditional offloading mechanism is not quite suitable for the success of the entire process in terms of assurance of QoS. Machine learning is a form of an automated data analysis for developing analytical models [ 38 ]. That is why it enables the task offloading process to access hidden patterns, trends, and insights of the received tasks.…”

Section: Proposed Research Methodologymentioning

confidence: 99%

An Intelligent Proposed Model for Task Offloading in Fog-Cloud Collaboration Using Logistics Regression

Bukhari

Ghazal

Abbas

et al. 2022

Computational Intelligence and Neuroscience

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Smart applications and intelligent systems are being developed that are self-reliant, adaptive, and knowledge-based in nature. Emergency and disaster management, aerospace, healthcare, IoT, and mobile applications, among them, revolutionize the world of computing. Applications with a large number of growing devices have transformed the current design of centralized cloud impractical. Despite the use of 5G technology, delay-sensitive applications and cloud cannot go parallel due to exceeding threshold values of certain parameters like latency, bandwidth, response time, etc. Middleware proves to be a better solution to cope up with these issues while satisfying the high requirements task offloading standards. Fog computing is recommended middleware in this research article in view of the fact that it provides the services to the edge of the network; delay-sensitive applications can be entertained effectively. On the contrary, fog nodes contain a limited set of resources that may not process all tasks, especially of computation-intensive applications. Additionally, fog is not the replacement of the cloud, rather supplement to the cloud, both behave like counterparts and offer their services correspondingly to compliance the task needs but fog computing has relatively closer proximity to the devices comparatively cloud. The problem arises when a decision needs to take what is to be offloaded: data, computation, or application, and more specifically where to offload: either fog or cloud and how much to offload. Fog-cloud collaboration is stochastic in terms of task-related attributes like task size, duration, arrival rate, and required resources. Dynamic task offloading becomes crucial in order to utilize the resources at fog and cloud to improve QoS. Since this formation of task offloading policy is a bit complex in nature, this problem is addressed in the research article and proposes an intelligent task offloading model. Simulation results demonstrate the authenticity of the proposed logistic regression model acquiring 86% accuracy compared to other algorithms and confidence in the predictive task offloading policy by making sure process consistency and reliability.

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An Improved Artificial Neural Network Model for Effective Diabetes Prediction

Cited by 83 publications

References 14 publications

A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques

A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques

Impact of Activation, Optimization, and Regularization Methods on the Facial Expression Model Using CNN

An Intelligent Proposed Model for Task Offloading in Fog-Cloud Collaboration Using Logistics Regression

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