Hybrid artificial fish particle swarm optimizer and kernel extreme learning machine for type-II diabetes predictive model

Kanimozhi, N.; Singaravel, G.

doi:10.1007/s11517-021-02333-x

Cited by 11 publications

(5 citation statements)

References 38 publications

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“…Because of its quick learning speed, good generalization capabilities, and ease of application, ELM has stimulated interest of a wide range of sectors. This model has been applied in palmprint recognition, 23 medical treatment, [24][25][26][27][28] motion image classification, 29 communication networks, 30 environmental management, 31 water quality detection, 32 and…”

Section: Discussionmentioning

confidence: 99%

Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine

Liu¹,

Wang²,

Li³

et al. 2022

DMSO

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The common cause of blindness in people with type 2 diabetes (T2D) is diabetic retinopathy (DR). Early fundus examinations have been shown to prevent vision loss, but routine ophthalmic screenings for patients with diabetes present significant financial and material challenges to existing health-care systems. The purpose of this study is to build a DR prediction model based on the extreme learning machine (ELM) and to compare the performance with the DR prediction models based on support machine vector (SVM), K proximity (KNN), random forest (RF) and artificial neural network (ANN). Methods: From January 1, 2020 to November 31, 2021, data were collected from electronic inpatient medical records at Lu'an Hospital of Anhui Medical University in China. An extreme learning machine (ELM) algorithm was used to develop a prediction model based on demographic data and blood testing and urine test results. Several metrics were used to evaluate the model's performance: (1) classification accuracy (ACC), (2) sensitivity, (3) specificity, (4) Precision,(5) Negative predictive value (NPV), ( 6) Training time and (7) area under the receiver operating characteristic (ROC) curve (AUC). Results: In terms of ACC, Sensitivity, Specificity, Precision, NPV and AUC, DR prediction model based on SVM and ELM is better than DR prediction model based on ANN, KNN and RF. The prediction model for diabetic retinopathy based on elm is the best among them in terms of ACC, Precision, Specificity, Training time and AUC, with 84.45%, 83.93%, 93.16%,1.24s, and 88.34%, respectively. The DR prediction model based on SVM is the best in terms of sensitivity and NPV, which are, respectively, 70.82% and 85.60%. Conclusion: According to the findings of this study, the model based on the extreme learning machine presents an outstanding performance in predicting diabetic retinopathy thus providing technological assistance for screening of diabetic retinopathy.

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Section: Discussionmentioning

confidence: 99%

Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine

Liu¹,

Wang²,

Li³

et al. 2022

DMSO

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“…Different measurements such as accuracy, sensitivity, specificity, Mathews Correlation Coefficient, and Kappa Statistics are evaluated to test the suggested approach. The findings indicate that the KELM-HAFPSO method is a potential novel technique for the diagnosis of type 2 diabetes [13]. The general PSO is a well-known swarm-based algorithm.…”

Section: Introductionmentioning

confidence: 95%

Optimizing Multi-Layer Perceptron using Variable Step Size Firefly Optimization Algorithm for Diabetes Data Classification

Behera¹,

Sarangi²,

Mishra³

et al. 2023

Int. J. Onl. Eng.

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According to a survey conducted by the International Diabetes Federation, the proportion of people living with diabetes is gradually rising. Diabetes mellitus is a chronic disorder caused by elevated blood sugar levels. For the early diagnosis and treatment of diabetes patients, efficient machine-learning methods are needed. Data Classification is a significant subject in many areas of life, and it is also a very challenging job in data mining. Clinical data mining has recently gained attention in complicated healthcare challenges relying on healthcare datasets. The principal objective of classification is to classify all data in a given dataset to a certain class label. In the healthcare field, classification is commonly employed in much research articles. A hybrid method for diabetes data classification is suggested by integrating multilayer perceptron with a modified firefly optimization algorithm for diabetes data classification. The performance of the proposed hybrid multilayer perceptron variable step size firefly algorithm is compared with other hybrid models such as the hybrid multilayer perceptron particle swarm optimization algorithm, hybrid multilayer perceptron differential evolution algorithm, and hybrid multilayer perceptron firefly optimization algorithm. The performance of these models is calculated based on accuracy, precision, recall, F1 score, and mean square error. In comparison to other models, the proposed hybrid model produces superior outcomes for diabetes data classification.

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“…Literature [28] proposed the MFO and KELM model, which can be effectively used for soil erosion prediction. Literature [29] proposed a hybrid artifcial fsh particle swarm optimization and KELM model for type II diabetes prediction. Te sparrow search algorithm (SSA) is a new type of swarm optimization algorithm [30], mimicking the foraging and antipredation behaviors of a sparrow group.…”

Section: Introductionmentioning

confidence: 99%

Air Target Threat Assessment: A Kernel Extreme Learning Machine Based on a Multistrategy Improved Sparrow Search Algorithm

Song

Liu

Xue

et al. 2023

Mathematical Problems in Engineering

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Air strikes are among the main means of attack in modern warfare. To improve air defense capabilities and aid military decision-making, threat assessment models have been introduced. As the parameters of the kernel extreme learning machine (KELM) model need to be set individually, this study proposes a parameter learning strategy based on a multistrategy improved sparrow search algorithm (MISSA). First, a reasonable threat assessment model was established based on the capability and situation factors of air targets. Second, the sparrow search algorithm was improved in terms of population position initialization and position update strategy, incorporating tent chaos reverse learning, nonlinear inertia weights, a global search strategy, and adaptive t-distribution. The effectiveness of the MISSA strategy was verified using nine common benchmark functions. The results show that the proposed MISSA finds an effective balance between global and local searches. Moreover, when the MISSA is applied to solve the tuning problem of KELM, the values of mean absolute percentage error, mean square error, root mean square error, and mean absolute error for MISSA–KELM in the air target threat assessment problem are 2.013 × 10−2, 1.282 × 10−4, 1.132 × 10−2, and 8.316 × 10−3, respectively, all of which are higher than that of the other metaheuristic algorithms (e.g., ACWOA-KELM and HGWO-KELM). Therefore, the method proposed in this study can be used as a parameter-tuning tool for KELM, enabling KELM to perform better in practical applications.

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Hybrid artificial fish particle swarm optimizer and kernel extreme learning machine for type-II diabetes predictive model

Cited by 11 publications

References 38 publications

Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine

Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine

Optimizing Multi-Layer Perceptron using Variable Step Size Firefly Optimization Algorithm for Diabetes Data Classification

Air Target Threat Assessment: A Kernel Extreme Learning Machine Based on a Multistrategy Improved Sparrow Search Algorithm

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