Forecasting hyponatremia in hospitalized patients using multilayer perceptron and multivariate linear regression techniques

Theerthagiri, Prasannavenkatesan

doi:10.1002/cpe.6248

Cited by 17 publications

(6 citation statements)

References 27 publications

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“…e results of three prediction models-NAR, the prophet time-series prediction framework [65][66][67], and linear regression-were compared, and the prediction effect of the algorithm's Stage 1 was evaluated. Figure 10 depicts the results.…”

Section: Comparison and Verification Of Methodsmentioning

confidence: 99%

Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA‐III

Chen

2022

Complexity

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Establishing a rapid-response mechanism to manage customer orders is very important in managing demand surges. In this study, combined with predicting order requests, we established a multiobjective optimization model to solve the warehouse space allocation problem. First, we developed a model based on the NAR neural network to predict order requests. Subsequently, we used the improved NSGA-III based on good point set theory to construct a multiobjective optimization model to minimize resource loss, maximize efficiency in goods selection, and maximize goods accumulation. The following three modes were tested to allocate warehouse storage space: random, ABC, and prediction-oriented. Finally, using actual order data, we conducted a comparative analysis of the three modes regarding their efficiency in goods selection. The method proposed by this study improved goods selection efficiency by a sizable margin (23.8%).

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Section: Comparison and Verification Of Methodsmentioning

confidence: 99%

Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA‐III

Chen

2022

Complexity

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“…Typically, the performance of the machine learning prediction algorithms measured by using some metrics based on the classification algorithm. In this work, the prediction results are evaluated by using the metrics such as accuracy, mean square error (MSE), root mean square error (RMSE), Kappa score, confusion matrix, the receiver operating characteristic area under curve (ROC_AUC), classification performance indices, sensitivity, specificity, and f1 score values [18,20,23].…”

Section: Performance Evaluationmentioning

confidence: 99%

Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms

2022

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Diabetes mellitus is characterized as a chronic disease that may cause many complications. Machine learning algorithms are used to diagnose and predict diabetes. The learning-based algorithms play a vital role in supporting decision-making in disease diagnosis and prediction. In this paper, traditional classification algorithms and neural network-based machine learning are investigated for the diabetes dataset. Also, various performance methods with different aspects are evaluated for the K-nearest neighbor, Naive Bayes, extra trees, decision trees, radial basis function, and multilayer perceptron algorithms. It supports the estimation of patients who possibly suffer from diabetes in the future. This work shows that the multilayer perceptron algorithm gives the highest prediction accuracy with the lowest MSE of 0.19. The MLP gives the lowest false positive rate and false-negative rate with the highest area under curve of 86 %.

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“…The supervised learning analyzes the past values of a node given input as a sequence of real values of nodes. For the given timestamp, each neuron calculates its current activation with the nonlinear function 23–25 …”

Section: Mobility Speed Predictionmentioning

confidence: 99%

Mobility speed prediction using ARIMA and RNN for random walk mobility model in mobile ad hoc networks

Theerthagiri

Thangavelu²

2021

Concurrency and Computation

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In this article, an auto‐regressive integrated moving average (ARIMA) modeling has been proposed to predict the mobility speed of the nodes in the mobile ad hoc networks (MANETs). The mobility speed prediction supports effective route discovery to enhance efficient and reliable routing. The random walk mobility model had been used to forecast the mobility of the nodes. Subsequently, various the recurrent neural network (RNN) model has been developed with different number hidden neurons, and the prediction results are compared with proposed ARIMA based results. The Akaike information criterion, auto‐correlation function metrics are evaluated to assess the dataset's quality. Several network scenarios are evaluated with various speeds and the number of nodes. The results of the proposed ARIMA model are compared with the RNN results. It demonstrates that the proposed model has higher prediction rates with reduced error rates of 0.1%–1.4% than RNN values. Such that the proposed methods facilitate the higher prediction rate of 10%–26% as compared with the RNN. Therefore, the proposed mobility speed prediction techniques support the effective routing in the MANETs.

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Forecasting hyponatremia in hospitalized patients using multilayer perceptron and multivariate linear regression techniques

Cited by 17 publications

References 27 publications

Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA‐III

Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA‐III

Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms

Mobility speed prediction using ARIMA and RNN for random walk mobility model in mobile ad hoc networks

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