Enhancing deep vein thrombosis prediction in patients with coronavirus disease 2019 using improved machine learning model

BACKGROUND

Deep vein thrombosis (DVT) is a significant complication in coronavirus disease 2019 patients, arising from coagulation issues in the deep venous system. Among 424 scheduled patients, 202 developed DVT (47.64%). DVT increases hospitalization risk, and complications, and impacts prognosis. Accurate prognostication and timely intervention are crucial to prevent DVT progression and improve patient outcomes.

METHODS

This study introduces an effective DVT prediction model, named bSES-AC-RUN-FKNN, which integrates fuzzy k-nearest neighbor (FKNN) with enhanced Runge-Kutta optimizer (RUN). Recognizing the insufficient effectiveness of RUN in local search capability and its convergence accuracy, spherical evolutionary search (SES) and differential evolution-inspired knowledge adaptive crossover (AC) are incorporated, termed SES-AC-RUN, to enhance its optimization capability.

RESULTS

Based on the benchmark set by CEC 2017 and comparative analyses with several peers, it is evident that SES-AC-RUN significantly enhances search performance compared to traditional RUN, even standing comparably against leading championship algorithms. The proposed bSES-AC-RUN-FKNN model was applied to predict a dataset comprising 424 cases of DVT patients, totaling 7208 records. Remarkably, the model demonstrates outstanding accuracy, reaching 91.02%, alongside commendable sensitivity at 91.07%.

CONCLUSIONS

The bSES-AC-RUN-FKNN emerges as a robust and efficient predictive tool, significantly enhancing the accuracy of DVT prediction. This model can be used to manage the risk of thrombosis in the care of COVID-19 patients. Nursing staff can combine the model's predictions with clinical judgment to formulate comprehensive treatment approaches.

A Novel Hybrid Runge Kutta Optimizer with Support Vector Machine on Gene Expression Data for Cancer Classification

It is crucial to accurately categorize cancers using microarray data. Researchers have employed a variety of computational intelligence approaches to analyze gene expression data. It is believed that the most difficult part of the problem of cancer diagnosis is determining which genes are informative. Therefore, selecting genes to study as a starting point for cancer classification is common practice. We offer a novel approach that combines the Runge Kutta optimizer (RUN) with a support vector machine (SVM) as the classifier to select the significant genes in the detection of cancer tissues. As a means of dealing with the high dimensionality that characterizes microarray datasets, the preprocessing stage of the ReliefF method is implemented. The proposed RUN-SVM approach is tested on binary-class microarray datasets (Breast2 and Prostate) and multi-class microarray datasets in order to assess its efficacy (i.e., Brain Tumor1, Brain Tumor2, Breast3, and Lung Cancer). Based on the experimental results obtained from analyzing six different cancer gene expression datasets, the proposed RUN-SVM approach was found to statistically beat the other competing algorithms due to its innovative search technique.

An enhanced Runge Kutta boosted machine learning framework for medical diagnosis

With the development and maturity of machine learning methods, medical diagnosis aided with machine learning methods has become a popular method to assist doctors in diagnosing and treating patients. However, machine learning methods are greatly affected by their hyperparameters, for instance, the kernel parameter in kernel extreme learning machine (KELM) and the learning rate in residual neural networks (ResNet). If the hyperparameters are appropriately set, the performance of the classifier can be significantly improved. To boost the performance of the machine learning methods, this paper proposes to improve the Runge Kutta optimizer (RUN) to adaptively adjust the hyperparameters of the machine learning methods for medical diagnosis purposes. Although RUN has a solid mathematical theoretical foundation, there are still some performance defects when dealing with complex optimization problems. To remedy these defects, this paper proposes a new enhanced RUN method with a grey wolf mechanism and an orthogonal learning mechanism called GORUN. The superior performance of the GORUN was validated against other well-established optimizers on IEEE CEC 2017 benchmark functions. Then, the proposed GORUN is employed to optimize the machine learning models, including the KELM and ResNet, to construct robust models for medical diagnosis. The performance of the proposed machine learning framework was validated on several medical data sets, and the experimental results have demonstrated its superiority.