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 4 publications

(5 citation statements)

References 29 publications

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“…It seeks an optimal separator level that matches the data's dimensions for binary classification, categorizing training into two classes. In the case of more than two classes, the algorithm employs multiclass SVM [22]. The fundamental concept of SVM involves constructing an optimal level in a space to address classification challenges and distinguish between models.…”

Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

Survey of Dairy Products Using Machine Learning Techniques

Khaliq Fadel,

Bahnam

2024

IRJIET

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The quality of dairy is of great importance in the food industry, as dairy is one of the main sources of proteins, calcium, vitamins and minerals that the human body needs. Dairy is found in many products, including milk, cream, and cheese, the most important of which is milk. It represents the basic and important element in people's lives, especially children, because it is an indispensable source for their growth, building their bones, and strengthening their bodies. Milk is a perishable product. Every gram of milk of poor quality or structure can cause tons of milk to spoil, causing significant financial losses and can lead to poisoning. Therefore, the quality and safety standards of this product are essential to ensure the provision of healthy and safe products to consumers. Determining the quality of the milk product is crucial for the purpose of monitoring to reduce potential losses and damages. The aim of this research is to provide a brief survey of the methods used in classifying milk quality using artificial intelligence methods, which include the use of machine learning algorithms through Identifying the most important and accurate methods and displaying the results reached using the known metrics: Accuracy, Precision, Recall, and F1_score.

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Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

Survey of Dairy Products Using Machine Learning Techniques

Khaliq Fadel,

Bahnam

2024

IRJIET

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“…The article indicates that the fuzzy system and the deep learning method could also be used to improve the proposed method. In the investigation [15], dataset processing is performed to detect and diagnose diabetes mellitus, focusing on the use of machine learning algorithms. The investigation of a hybrid model where a coyote optimization algorithm (COA) and least squares support vector machine (LS-SVM) was proposed, where an average accuracy of 98.811% was obtained outperforming the other algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive machine learning applying cross industry standard process for data mining for the diagnosis of diabetes mellitus type 2

Garcia-Rios¹,

Marres-Salhuana²,

Sierra-Liñan

et al. 2023

IJ-AI

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Currently, type 2 diabetes mellitus is one of the world's most prevalent diseases and has claimed millions of people's lives. The present research aims to know the impact of the use of machine learning in the diagnostic process of type 2 diabetes mellitus and to offer a tool that facilitates the diagnosis of the dis-ease quickly and easily. Different machine learning models were designed and compared, being random forest was the algorithm that generated the model with the best performance (90.43% accuracy), which was integrated into a web platform, working with the PIMA dataset, which was validated by specialists from the Peruvian League for the Fight against Diabetes organization. The result was a decrease of (A) 88.28% in the information collection time, (B) 99.99% in the diagnosis time, (C) 44.42% in the diagnosis cost, and (D) 100% in the level of difficulty, concluding that the application of machine learning can significantly optimize the diagnostic process of type 2 diabetes mellitus.

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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%

“…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

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

Cited by 4 publications

References 29 publications

Survey of Dairy Products Using Machine Learning Techniques

Survey of Dairy Products Using Machine Learning Techniques

Predictive machine learning applying cross industry standard process for data mining for the diagnosis of diabetes mellitus type 2

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

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