An Optimized Hyperparameter of Convolutional Neural Network Algorithm for Bug Severity Prediction in Alzheimer’s-Based IoT System

Abstract

Softwares are involved in all aspects of healthcare, such as booking appointments to software systems that are used for treatment and care of patients. Many vendors and consultants develop high quality software healthcare systems such as hospital management systems, medical electronic systems, and middle-ware softwares in medical devices. Internet of Things (IoT) medical devices are gaining attention and facilitate the people with new technology. The health condition of the patients are monitored by the IoT devices using sensors, specifically brain diseases such as Alzheimer, Parkinson’s, and Traumatic brain injury. Embedded software is present in IoT medical devices and the complexity of software increases day-by-day with the increase in the number and complexity of bugs in the devices. Bugs present in IoT medical devices can have severe consequences such as inaccurate records, circulatory suffering, and death in some cases along with delay in handling patients. There is a need to predict the impact of bugs (severe or nonsevere), especially in case of IoT medical devices due to their critical nature. This research proposes a hybrid bug severity prediction model using convolution neural network (CNN) and Harris Hawk optimization (HHO) based on an optimized hyperparameter of CNN with HHO. The dataset is created, that consists of the bugs present in healthcare systems and IoT medical devices, which is used for evaluation of the proposed model. A preprocessing technique on textual dataset is applied along with a feature extraction technique for CNN embedding layer. In HHO, we define the hyperparameter values of “Batch Size, Learning Rate, Activation Function, Optimizer Parameters, and Kernel Initializers,” before training the model. Hybrid model CNN-HHO is applied, and a 10-fold cross validation is performed for evaluation. Results indicate an accuracy of 96.21% with the proposed model.

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review

“…Some recent studies show that optimizing parameters improves model performance considerably, demonstrating the need to optimize simple models rather than building complex models from scratch ( Gao et al, 2020 ; Yousaf et al, 2022 ). If the problem is interpreted as an optimization problem, where the best configuration that maximizes the network performance is sought, a combination of search algorithms already available in the literature with a parameterized DL model can help to solve these problems.…”

Reviewing the Impact of Embedded Systems in Medical Devices in the Usa

“…Machine learning and predictive analytics play a crucial role in the future of embedded systems in medical devices. An optimized hyperparameter of the Convolutional Neural Network (CNN) algorithm has been applied for bug severity prediction in Alzheimer's-based IoT systems, demonstrating the potential for machine learning to enhance healthcare applications (Yousaf et al, 2022). Furthermore, the use of an ensemble approach for healthcare data analytics, incorporating classifiers such as K-Nearest Neighbor, Support Vector Machine, Random Forest, Naïve Bayes, and Logistic Regression, showcases the growing significance of machine learning in healthcare data analysis (Javale & Desai, 2022).…”