Genetic underpinnings explored: OPA1 deletion and complex phenotypes on chromosome 3q29.

BACKGROUND: Copy number variations (CNVs) have emerged as significant contributors to the elusive genetic causality of inherited eye diseases. In this study, we describe a case with optic atrophy and a brain aneurysm, in which a de novo CNV 3q29 deletion was identified. CASE PRESENTATION: A 40-year-old female patient was referred to our department after undergoing aneurysm transcatheter arterial embolization for a brain aneurysm. She had no history of systemic diseases, except for unsatisfactory best-corrected visual acuity (BCVA) since elementary school. Electrophysiological tests confirmed the findings in retinal images, indicating optic nerve atrophy. Chromosomal microarray analysis revealed a de novo deletion spanning 960 kb on chromosome 3q29, encompassing OPA1 and six neighboring genes. Unlike previously reported deletions in this region associated with optic atrophy, neuropsychiatric disorders, and obesity, this patient displayed a unique combination of optic atrophy and a brain aneurysm. However, there is no causal relationship between the brain aneurysm and the CNV. CONCLUSION: In conclusion, the optic atrophy is conclusively attributed to the OPA1 deletion, and the aneurysm could be a coincidental association. The report emphasizes the likelihood of underestimating OPA1 deletions due to sequencing technology limitations. Recognizing these constraints, healthcare professionals must acknowledge these limitations and consistently search for OPA1 variants/deletions in Autosomal Dominant Optic Atrophy (ADOA) patients with negative sequencing results. This strategic approach ensures a more comprehensive exploration of copy-number variations, ultimately enhancing diagnostic precision in the field of genetic disorders.

Optimized fuzzy K-nearest neighbor approach for accurate lung cancer prediction based on radial endobronchial ultrasonography.

Radial endobronchial ultrasonography (R-EBUS) has been a surge in the development of new ultrasonography for the diagnosis of pulmonary diseases beyond the central airway. However, it faces challenges in accurately pinpointing the location of abnormal lesions. Therefore, this study proposes an improved machine learning model aimed at distinguishing between malignant lung disease (MLD) from benign lung disease (BLD) through R-EBUS features. An enhanced manta ray foraging optimization based on elite perturbation search and cyclic mutation strategy (ECMRFO) is introduced at first. Experimental validation on 29 test functions from CEC 2017 demonstrates that ECMRFO exhibits superior optimization capabilities and robustness compared to other competing algorithms. Subsequently, it was combined with fuzzy k-nearest neighbor for the classification prediction of BLD and MLD. Experimental results indicate that the proposed modal achieves a remarkable prediction accuracy of up to 99.38%. Additionally, parameters such as R-EBUS1 Circle-dense sign, R-EBUS2 Hemi-dense sign, R-EBUS5 Onionskin sign and CCT5 mediastinum lymph node are identified as having significant clinical diagnostic value.

Diagnosis and therapy of giant epidermoid double cysts with infection on the buttock: A case report and literature review.

RATIONALE: Epidermoid cyst (EC) is a common clinical condition and it can be filled with keratinized material. EC often represents painless, slow progressive growth, and single cyst. The cyst is usually 1 to 5 cm in size. Giant epidermoid cysts on the buttock area are extremely rare, and reports of giant epidermoid double cysts on the buttock are even rarer. PATIENT CONCERNS: This paper reports a patient with a painless mass was on the left buttock. DIAGNOSIS: A giant epidermoid double cysts with infection in a left buttock paranal location. INTERVENTIONS: The mass was surgically removed. OUTCOMES: The patient recovered well after surgical treatment and currently has no recurrence. CONCLUSION: For patients with EC, MRI is recommended as a routine examination before surgery in order to detect the variation and extent of the cyst early. This lays a foundation for the complete resection of the lesion during the operation. The review of relevant literature will hopefully be helpful to clinicians.

Long non-coding RNA and Evolving drug resistance in lung cancer.

Non-small cell lung cancer (NSCLC) is one of the most devastating cancers with a high incidence and mortality rates of all cancers. Locally advanced or metastatic NSCLC patients can benefit from platinum-based chemotherapy and targeted therapy drugs. Nevertheless, primary or acquired drug resistance will result in ineffective treatment, leading to tumor progression. The detailed mechanism underlying drug resistance to NSCLC are complicated and result from various factor. Among them, long noncoding RNAs (lncRNAs) have been found to be critically involved in NSCLC development and play a vital role in mediating therapy resistance. In this review, we attempt to systematically summarize the mechanisms underlying the lncRNA-mediated resistance to chemotherapy agents and targeted therapy drugs against lung cancer.

Clinical Characteristics and Genetic Variants in Taiwanese Patients With PROM1-Related Inherited Retinal Disorders.

Purpose: This study investigated the clinical characteristics of patients with PROM1-related inherited retinal diseases (IRDs). Methods: Patients diagnosed with IRDs who had mutations in PROM1 were identified at Linkou Chang Gung Memorial Hospital and Kaohsiung Medical University Hospital in Taiwan. Information on clinical characteristics and best-corrected visual acuity was recorded. Color fundus (CF) images, fundus autofluorescence photography (FAF), spectral-domain optical coherence tomography (SD-OCT), and electroretinograms (ERGs) were analyzed to examine patient phenotypes. PROM1 variants were detected using whole exome sequencing and verified by Sanger sequencing. Results: Fourteen patients from nine families with PROM1-related IRDs were analyzed. Most patients exhibited chorioretinal atrophy in the macular area, with or without extramacular involvement on CF. Similarly, hypo-autofluorescence confined to the macular area, with or without extramacular involvement, was present for most patients on FAF. Furthermore, SD-OCT revealed outer retinal tubulations and focal or diffuse retinal thinning. ERGs showed variable findings, including maculopathy with normal ERG, subnormal cone response, and extinguished rod and cone responses. We detected five variants of the PROM1 gene, including c.139del, c.794del, c.1238T>A, c.2110C>T, and c.1117C>T. Conclusions: In this study, we evaluated 14 Taiwanese patients with five PROM1 variants. Additionally, incomplete penetrance of heterozygous PROM1 variants was observed. Furthermore, patients with autosomal dominant PROM1 variants had lesions in the macular area and the peripheral region of the retina. SD-OCT serves as a useful tool for early detection of PROM1-related IRDs, as it captures certain signs of such diseases.

Graph Reasoning Method Based on Affinity Identification and Representation Decoupling for Predicting lncRNA-Disease Associations.

An increasing number of studies have shown that dysregulation of lncRNAs is related to the occurrence of various diseases. Most of the previous methods, however, are designed based on homogeneity assumption that the representation of a target lncRNA (or disease) node should be updated by aggregating the attributes of its neighbor nodes. However, the assumption ignores the affinity nodes that are far from the target node. We present a novel prediction method, GAIRD, to fully leverage the heterogeneous information in the network and the decoupled node features. The first major innovation is a random walk strategy based on width-first searching and depth-first searching. Different from previous methods that only focus on homogeneous information, our new strategy learns both the homogeneous information within local neighborhoods and the heterogeneous information within higher-order neighborhoods. The second innovation is a representation decoupling module to extract the purer attributes and the purer topologies. Third, a module based on group convolution and deep separable convolution is developed to promote the pairwise intrachannel and interchannel feature learning. The experimental results show that GAIRD outperforms comparing state-of-the-art methods, and the ablation studies prove the contributions of major innovations. We also performed case studies on 3 diseases to further demonstrate the effectiveness of the GAIRD model in applications.

The Value of Electroretinography in Identifying Candidate Genes for Inherited Retinal Dystrophies: A Diagnostic Guide.

Inherited retinal dystrophies (IRDs) are a group of heterogeneous diseases caused by genetic mutations that specifically affect the function of the rod, cone, or bipolar cells in the retina. Electroretinography (ERG) is a diagnostic tool that measures the electrical activity of the retina in response to light stimuli, and it can help to determine the function of these cells. A normal ERG response consists of two waves, the a-wave and the b-wave, which reflect the activity of the photoreceptor cells and the bipolar and Muller cells, respectively. Despite the growing availability of next-generation sequencing (NGS) technology, identifying the precise genetic mutation causing an IRD can be challenging and costly. However, certain types of IRDs present with unique ERG features that can help guide genetic testing. By combining these ERG findings with other clinical information, such as on family history and retinal imaging, physicians can effectively narrow down the list of candidate genes to be sequenced, thereby reducing the cost of genetic testing. This review article focuses on certain types of IRDs with unique ERG features. We will discuss the pathophysiology and clinical presentation of, and ERG findings on, these disorders, emphasizing the unique role ERG plays in their diagnosis and genetic testing.

An optimized machine learning method for predicting wogonin therapy for the treatment of pulmonary hypertension.

Human health is at risk from pulmonary hypertension (PH), characterized by decreased pulmonary vascular resistance and constriction of the pulmonary vessels, resulting in right heart failure and dysfunction. Thus, preventing PH and monitoring its progression before treating it is vital. Wogonin, derived from the leaves of Scutellaria baicalensis Georgi, exhibits remarkable pharmacological activity. In this study, we examined the effectiveness of wogonin in mitigating the progression of PH in mice using right heart catheterization and hematoxylin-eosin (HE) staining. As an alternative to minimize the possibility of harming small animals, we present a scientifically effective feature selection method (BSCDWOA-KELM) that will allow us to develop a novel simpler noninvasive prediction method for wogonin in treating PH. In this method, we use the proposed enhanced whale optimizer (SCDWOA) in conjunction with the kernel extreme learning machine (KELM). Initially, we let SCDWOA perform global optimization experiments on the IEEE CEC2014 benchmark function set to verify its core advantages. Lastly, 12 public and PH datasets are examined for feature selection experiments using BSCDWOA-KELM. As shown in the experimental results for global optimization, the proposed SCDWOA has better convergence performance. Meanwhile, the proposed binary SCDWOA (BSCDWOA) significantly improves the ability of KELM to classify data. By utilizing the BSCDWOA-KELM, key indicators such as the Red blood cell (RBC), the Haemoglobin (HGB), the Lymphocyte percentage (LYM%), the Hematocrit (HCT), and the Red blood cell distribution width-size distribution (RDW-SD) can be efficiently screened in the Pulmonary hypertension dataset, and one of its most essential points is its accuracy of greater than 0.98. Consequently, the BSCDWOA-KELM introduced in this study can be used to predict wogonin therapy for treating pulmonary hypertension in a simple and noninvasive manner.

Progress in the surgical treatment of sacrococcygeal pilonidal sinus: a review.

BACKGROUND: A pilonidal sinus (PS) is an acquired disease resulting from recurrent infections and chronic inflammation. A PS involving the sacrococcyx is referred to as a sacrococcygeal PS (SPS). An SPS is a rare chronic infectious disease for which surgery is a good choice. The incidence of SPS has gradually increased worldwide in recent years. However, surgeons have not reached a consensus on the preferred surgical approach for SPS. The authors performed a systematic review and meta-analysis to analyze differences in the efficacy of different surgical approaches for the treatment of SPS. METHODS: A systematic search was conducted in the PubMed database covering the period from 1 January 2003, to 28 February 2023. The primary outcome parameters were recurrence and infection. Finally, statistical analysis (meta-analysis) was carried out using RevMan 5.4.1 software. In addition, we systematically reviewed the latest progress in the surgical treatment of SPS over the past 20 years, especially as reported in the past 3 years. RESULTS: Twenty-seven articles, 54 studies, and 3612 participants were included in this meta-analysis. The recurrence rate following the midline closure (MC) technique was much higher than that of other techniques. Among the techniques analyzed, the differences between MC and Limberg flap (LF), and between MC and marsupialization were statistically significant [ P =0.0002, risk ratio (RR)=6.15, 95% CI 2.40, 15.80; P =0.01, RR=12.70, 95% CI 1.70, 95.06]. The recurrence rate of open healing was higher than that of the Karydakis flap (KF) technique, and the difference was statistically significant ( P =0.02, RR=6.04, 95% CI 1.37, 26.55). Most of the results comparing MC with other techniques suggested that the former had a higher infection rate, and the difference between MC and LF was statistically significant ( P =0.0005, RR=4.14, 95% CI 1.86, 9.23). Comparison between KF and LF, modified LF and KF showed that the differences were not statistically significant in terms of recurrence and infection ( P ≥0.05). CONCLUSIONS: There are various surgical treatment options for SPS, including incision and drainage, excision of diseased tissue with primary closure and secondary healing, and minimally invasive surgery. It is still not possible to determine which surgical technique should be considered the gold standard for treatment, as even the results of different researchers using the same operation method are conflicting. But what is certain is that the midline closure technique has a much higher incidence of postoperative recurrence and infection than other techniques. Therefore, the anorectal surgeon should formulate the most suitable individualized plan for the patient based on a comprehensive evaluation of the patient's wishes, appearance of the SPS, and the professional ability of the surgeon.

Multiphase holdup measurement of oil-gas-water flow using new dual receiver fiber-optical probe array multiphase logging tool.

To solve the problem of multiphase holdup measurement, a new dual-receiver fiber-optical probe array multiphase logging tool (NDRFOPA_MLT) is designed and developed. This paper first constructed the mechanism model of an NDRFOP for phase holdup measurement by using the ray tracing method and theoretically analyzed the feasibility of NDRFOP to measure phase holdup; considering the shortcomings of NDRFOP local measurement, a NDRFOPA sensor for oil production three-phase flow is designed and developed. At the same time, the volume of fluid model was used to simulate the distribution characteristics of the medium in the NDRFOPA_MLT measurement pipeline under the working conditions of oil-gas-water flow with a total flow rate range of 0.42-1.25 m3/h, water holdup range of 50%-80%, oil holdup range of 10%-30%, and gas holdup range of 10%-40%. In addition, the NDRFOPA_MLT measurement models for different multiphase flow conditions were established by the ZEMAX ray tracing method, and the sensitivity distribution, response characteristics, and phase holdup measurement methods were studied to obtain the phase holdup measurement results under multiphase flow conditions. Finally, a multiphase flow experimental platform with a measurement pipe diameter of 20 mm and a measurement pipe length of 300 mm was established, and experiments were conducted under multiphase flow conditions, such as a gas flow rate range of 0.04-0.16 m3/h, oil flow rate range of 0.64-1.70 m3/h, and water flow rate range of 0.53-2.58 m3/h. The experimental results showed that phase holdup measurement error was mainly kept within 10%.

Identification of Pulmonary Hypertension Animal Models Using a New Evolutionary Machine Learning Framework Based on Blood Routine Indicators.

Pulmonary Hypertension (PH) is a global health problem that affects about 1% of the global population. Animal models of PH play a vital role in unraveling the pathophysiological mechanisms of the disease. The present study proposes a Kernel Extreme Learning Machine (KELM) model based on an improved Whale Optimization Algorithm (WOA) for predicting PH mouse models. The experimental results showed that the selected blood indicators, including Haemoglobin (HGB), Hematocrit (HCT), Mean, Platelet Volume (MPV), Platelet distribution width (PDW), and Platelet-Large Cell Ratio (P-LCR), were essential for identifying PH mouse models using the feature selection method proposed in this paper. Remarkably, the method achieved 100.0% accuracy and 100.0% specificity in classification, demonstrating that our method has great potential to be used for evaluating and identifying mouse PH models.

Effects of TIMP-2 Polymorphisms on Retinopathy of Prematurity Risk, Severity, Recurrence, and Treatment Response.

Tissue inhibitors of metalloproteinases (TIMPs) play a crucial role in endogenous angiogenesis besides the regulation of matrix metalloproteinase (MMP) activity. Associations between TIMP-2 gene polymorphisms and the risk of retinopathy of prematurity (ROP) were examined. Premature infants born between 2009 and 2018 were included. Five single-nucleotide polymorphisms (SNPs) of TIMP-2 were analyzed with real-time polymerase chain reaction (PCR). Multivariate logistic regression was applied to model associations between TIMP-2 polymorphisms and ROP susceptibility and severity. The GA+AA genotype in individuals with the TIMP-2 polymorphism of rs12600817 was associated with a higher risk of ROP (odds ratio [OR]: 1.518, 95% confidence interval [CI]: 1.028-2.242) compared with their wild-type genotypes. The AA genotype (OR: 1.962, 95% CI: 1.023-3.762) and the AA+GA genotype (OR: 1.686, 95% CI: 1.030-2.762) in individuals with the rs12600817 polymorphism had higher risks of severe, treatment-requiring ROP relative to their wild-type counterparts. In patients with treatment-requiring ROP, the AG+GG genotypes in the TIMP-2 polymorphism of rs2889529 were correlated with the treatment response (p = 0.035). The TIMP-2 polymorphism of rs12600817 help in predicting ROP risks in preterm infants, while the polymorphism of rs2889529 can serve as a genetic marker in evaluating the ROP treatment response.

Learning global dependencies and multi-semantics within heterogeneous graph for predicting disease-related lncRNAs.

MOTIVATION: Long noncoding RNAs (lncRNAs) play an important role in the occurrence and development of diseases. Predicting disease-related lncRNAs can help to understand the pathogenesis of diseases deeply. The existing methods mainly rely on multi-source data related to lncRNAs and diseases when predicting the associations between lncRNAs and diseases. There are interdependencies among node attributes in a heterogeneous graph composed of all lncRNAs, diseases and micro RNAs. The meta-paths composed of various connections between them also contain rich semantic information. However, the existing methods neglect to integrate attribute information of intermediate nodes in meta-paths. RESULTS: We propose a novel association prediction model, GSMV, to learn and deeply integrate the global dependencies, semantic information of meta-paths and node-pair multi-view features related to lncRNAs and diseases. We firstly formulate the global representations of the lncRNA and disease nodes by establishing a self-attention mechanism to capture and learn the global dependencies among node attributes. Second, starting from the lncRNA and disease nodes, respectively, multiple meta-pathways are established to reveal different semantic information. Considering that each meta-path contains specific semantics and has multiple meta-path instances which have different contributions to revealing meta-path semantics, we design a graph neural network based module which consists of a meta-path instance encoding strategy and two novel attention mechanisms. The proposed meta-path instance encoding strategy is used to learn the contextual connections between nodes within a meta-path instance. One of the two new attention mechanisms is at the meta-path instance level, which learns rich and informative meta-path instances. The other attention mechanism integrates various semantic information from multiple meta-paths to learn the semantic representation of lncRNA and disease nodes. Finally, a dilated convolution-based learning module with adjustable receptive fields is proposed to learn multi-view features of lncRNA-disease node pairs. The experimental results prove that our method outperforms seven state-of-the-art comparing methods for lncRNA-disease association prediction. Ablation experiments demonstrate the contributions of the proposed global representation learning, semantic information learning, pairwise multi-view feature learning and the meta-path instance encoding strategy. Case studies on three cancers further demonstrate our method's ability to discover potential disease-related lncRNA candidates. CONTACT: zhang@hlju.edu.cn or peiliangwu@ysu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online.

An evolutionary machine learning for pulmonary hypertension animal model from arterial blood gas analysis.

Pulmonary hypertension (PH) is a rare and fatal condition that leads to right heart failure and death. The pathophysiology of PH and potential therapeutic approaches are yet unknown. PH animal models' development and proper evaluation are critical to PH research. This work presents an effective analysis technology for PH from arterial blood gas analysis utilizing an evolutionary kernel extreme learning machine with multiple strategies integrated slime mould algorithm (MSSMA). In MSSMA, two efficient bee-foraging learning operators are added to the original slime mould algorithm, ensuring a suitable trade-off between intensity and diversity. The proposed MSSMA is evaluated on thirty IEEE benchmarks and the statistical results show that the search performance of the MSSMA is significantly improved. The MSSMA is utilised to develop a kernel extreme learning machine (MSSMA-KELM) on PH from arterial blood gas analysis. Comprehensively, the proposed MSSMA-KELM can be used as an effective analysis technology for PH from arterial Blood gas analysis with an accuracy of 93.31%, Matthews coefficient of 90.13%, Sensitivity of 91.12%, and Specificity of 90.73%. MSSMA-KELM can be treated as an effective approach for evaluating mouse PH models.

Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine.

Coronavirus disease-2019 (COVID-19) has made the world more cautious about widespread viruses, and a tragic pandemic that was caused by a novel coronavirus has harmed human beings in recent years. The new coronavirus pneumonia outbreak is spreading rapidly worldwide. We collect arterial blood samples from 51 patients with a COVID-19 diagnosis. Blood gas analysis is performed using a Siemens RAPID Point 500 blood gas analyzer. To accurately determine the factors that play a decisive role in the early recognition and discrimination of COVID-19 severity, a prediction framework that is based on an improved binary Harris hawk optimization (HHO) algorithm in combination with a kernel extreme learning machine is proposed in this paper. This method uses specular reflection learning to improve the original HHO algorithm and is referred to as HHOSRL. The experimental results show that the selected indicators, such as age, partial pressure of oxygen, oxygen saturation, sodium ion concentration, and lactic acid, are essential for the early accurate assessment of COVID-19 severity by the proposed feature selection method. The simulation results show that the established methodlogy can achieve promising performance. We believe that our proposed model provides an effective strategy for accurate early assessment of COVID-19 and distinguishing disease severity. The codes of HHO will be updated in https://aliasgharheidari.com/HHO.html.

Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques.

Introduction: Pulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients. Methods: Combining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed. Results: To confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital. Discussion: The experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model's accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE.

A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China.

Introduction: Pulmonary embolism (PE) is a common thrombotic disease and potentially deadly cardiovascular disorder. The ratio of clinical misdiagnosis and missed diagnosis of PE is very large because patients with PE are asymptomatic or non-specific. Methods: Using the clinical data from the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China), we proposed a swarm intelligence algorithm-based kernel extreme learning machine model (SSACS-KELM) to recognize and discriminate the severity of the PE by patient's basic information and serum biomarkers. First, an enhanced method (SSACS) is presented by combining the salp swarm algorithm (SSA) with the cuckoo search (CS). Then, the SSACS algorithm is introduced into the KELM classifier to propose the SSACS-KELM model to improve the accuracy and stability of the traditional classifier. Results: In the experiments, the benchmark optimization performance of SSACS is confirmed by comparing SSACS with five original classical methods and five high-performance improved algorithms through benchmark function experiments. Then, the overall adaptability and accuracy of the SSACS-KELM model are tested using eight public data sets. Further, to highlight the superiority of SSACS-KELM on PE datasets, this paper conducts comparison experiments with other classical classifiers, swarm intelligence algorithms, and feature selection approaches. Discussion: The experimental results show that high D-dimer concentration, hypoalbuminemia, and other indicators are important for the diagnosis of PE. The classification results showed that the accuracy of the prediction model was 99.33%. It is expected to be a new and accurate method to distinguish the severity of PE.

Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization.

Coronavirus 2019 (COVID-19) is an extreme acute respiratory syndrome. Early diagnosis and accurate assessment of COVID-19 are not available, resulting in ineffective therapeutic therapy. This study designs an effective intelligence framework to early recognition and discrimination of COVID-19 severity from the perspective of coagulation indexes. The framework is proposed by integrating an enhanced new stochastic optimizer, a brain storm optimizing algorithm (EBSO), with an evolutionary machine learning algorithm called EBSO-SVM. Fast convergence and low risk of the local stagnant can be guaranteed for EBSO with added by Harris hawks optimization (HHO), and its property is verified on 23 benchmarks. Then, the EBSO is utilized to perform parameter optimization and feature selection simultaneously for support vector machine (SVM), and the presented EBSO-SVM early recognition and discrimination of COVID-19 severity in terms of coagulation indexes using COVID-19 clinical data. The classification performance of the EBSO-SVM is very promising, reaching 91.9195% accuracy, 90.529% Matthews correlation coefficient, 90.9912% Sensitivity and 88.5705% Specificity on COVID-19. Compared with other existing state-of-the-art methods, the EBSO-SVM in this paper still shows obvious advantages in multiple metrics. The statistical results demonstrate that the proposed EBSO-SVM shows predictive properties for all metrics and higher stability, which can be treated as a computer-aided technique for analysis of COVID-19 severity from the perspective of coagulation.

Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods.

This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.

An Effective Machine Learning Approach for Identifying Non-Severe and Severe Coronavirus Disease 2019 Patients in a Rural Chinese Population: The Wenzhou Retrospective Study.

This paper has proposed an effective intelligent prediction model that can well discriminate and specify the severity of Coronavirus Disease 2019 (COVID-19) infection in clinical diagnosis and provide a criterion for clinicians to weigh scientific and rational medical decision-making. With indicators as the age and gender of the patients and 26 blood routine indexes, a severity prediction framework for COVID-19 is proposed based on machine learning techniques. The framework consists mainly of a random forest and a support vector machine (SVM) model optimized by a slime mould algorithm (SMA). When the random forest was used to identify the key factors, SMA was employed to train an optimal SVM model. Based on the COVID-19 data, comparative experiments were conducted between RF-SMA-SVM and several well-known machine learning algorithms performed. The results indicate that the proposed RF-SMA-SVM not only achieves better classification performance and higher stability on four metrics, but also screens out the main factors that distinguish severe COVID-19 patients from non-severe ones. Therefore, there is a conclusion that the RF-SMA-SVM model can provide an effective auxiliary diagnosis scheme for the clinical diagnosis of COVID-19 infection.