A proposed model for diabetes mellitus classification using  coyote optimization algorithm and least squares support vector  machine

Bahnam, Baydaa Sulaiman; Dawwod, Suhair Abd

doi:10.11591/ijai.v11.i3.pp1164-1174

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…38 Bahnam and Dawwod proposed a hybrid model for classification of diabetes mellitus (DM) patients by combining coyote optimization algorithm (COA) with LSSVM. 39 Jiang et al combined LSSVM optimized by improved bat algorithm (IBA)with kernelized principal component analysis (KPCA) for disease classification. 40 Ahmed et al proposed an improved Barnacle Mating Optimizer and combined with LSSVM to predict COVID-19 confirmed cases with total vaccination.…”

Section: Related Workmentioning

confidence: 99%

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A survival prediction model based on PCA-HSIDA-LSSVM for patients with esophageal squamous cell carcinoma

Wang,

Xia,

Ling

et al. 2023

Proc Inst Mech Eng H

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Esophageal squamous cell carcinoma (ESCC) is a type of cancer and has some of the highest rates of both incidence and mortality globally. Developing accurate models for survival prediction provides a basis clinical judgment and decision making, improving the survival status of ESCC patients. Although many predictive models have been developed, there is still lack of highly accurate survival prediction models for ESCC patients. This study proposes a novel survival prediction model for ESCC patients based on principal component analysis (PCA) and least-squares support vector machine (LSSVM) optimized by an improved dragonfly algorithm with hybrid strategy (HSIDA). The original 17 blood indicators are condensed into five new variables by PCA, reducing data dimensionality and redundancy. An improved dragonfly algorithm based on hybrid strategy is proposed, which addresses the limitations of dragonfly algorithm, such as slow convergence, low search accuracy and insufficient vitality of late search. The proposed HSIDA is used to optimize the regularization parameter and kernel parameter of LSSVM, improving the prediction accuracy of the model. The proposed model is validated on the dataset of 400 patients with ESCC in the clinical database of First Affiliated Hospital of Zhengzhou University and the State Key Laboratory of Esophageal Cancer Prevention and Control of Henan Province. The experiment results demonstrate that the proposed HSIDA-LSSVM has the best prediction performance than LSSVM, HSIDA-BP, IPSO-LSSVM, COA-LSSVM and IBA-LSSVM. The proposed model achieves the accuracy of 96.25%, sensitivity of 95.12%, specificity of 97.44%, precision of 97.50%, and F1 score of 96.30%.

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Section: Related Workmentioning

confidence: 99%

“…In a dataset of 400 samples, 320 samples are used as the training set, and 80 samples are used as the test set. IPSO-LSSVM, 38 COA-LSSVM, 39 IBA-LSSVM 40 are used for comparison to verify the superiority of the proposed method.…”

Section: Application Of Pca-hsida-lssvm On Escc Datasetmentioning

confidence: 99%

A survival prediction model based on PCA-HSIDA-LSSVM for patients with esophageal squamous cell carcinoma

Wang,

Xia,

Ling

et al. 2023

Proc Inst Mech Eng H

View full text Add to dashboard Cite

show abstract

“…Cell state ct and filtered input ot control the output layer, which selects the result. As a result of its excellent estimation accuracy and application in a variety of load forecasting requests [29], this topology has attracted a lot of interest. Riswantini and Nugraheni [30] introduced it for predicting the strength of typhoons.…”

Section: Related Workmentioning

confidence: 99%

A secure framework of blockchain technology using CNN long short-term memory hybrid deep learning model

Sekhar

Rajendran²

2022

IJEECS

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Generation Z is embracing blockchain technology, which is appropriate for the digital age. Internet of things (IoT) can benefit from blockchain technology IoT. The proliferation of IoT technology has led to breakthroughs in distributed system architecture. For the blockchain network to store, communicate, and exchange data, it needs a randomized data management system. This shows how difficult it may be to provide consistent and safe data replication in a distributed system, an issue blockchain technology may overcome. We need a solid prediction model that improves results. This article describes an innovative way to overcome the limitations of third-party transactions using Bitcoin. In this article, convolutional neural networks-long short term memory (CNN-LSTM) deep learning forecasting models are introduced. Convolutional layers help extract relevant data from instances. It has an long short-term memory (LSTM) layer, which lets it find long-and short term dependencies. The experiment's goal was to test the multivariate statistical model we suggested and compare its performance to well-established models. The addition of convolutional layers to a forecasting model may improve its accuracy, according to an experiment. The research shows that this strategy has a better chance of success and is more trustworthy than others.

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A proposed model for diabetes mellitus classification using coyote optimization algorithm and least squares support vector machine

Cited by 2 publications

References 29 publications

A survival prediction model based on PCA-HSIDA-LSSVM for patients with esophageal squamous cell carcinoma

A survival prediction model based on PCA-HSIDA-LSSVM for patients with esophageal squamous cell carcinoma

A secure framework of blockchain technology using CNN long short-term memory hybrid deep learning model

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