The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity

The value of common blood parameters for the differential diagnosis of respiratory tract infections in children

Mycoplasma pneumoniae and influenza A virus are common pathogens that cause respiratory tract infection in children. Both pathogens present with similar clinical symptoms, and their epidemic periods often overlap. Consequently, it is challenging for clinicians to make a rapid preliminary diagnosis. However, common blood tests is simple and efficient, Therefore, the purpose of this study is to preliminarily distinguish Mycoplasma pneumoniae and influenza A virus infection in children by analyzing the results of common blood tests, thereby guiding clinical diagnosis and treatment.The results showed that, compared with children in the influenza A virus-positive group, children in the Mycoplasma pneumoniae-positive group had higher white blood cell (WBC), red blood cell (RBC), haemoglobin (HGB), platelet (PLT) counts, lymphocyte (LYM) and eosinophil (EOS) counts and ratios, as well as higher concentrations of C-reactive protein (CRP) and serum amyloid A (SAA), while neutrophil (NEU) and monocyte (MONO) counts and ratios, Neutrophil to Lymphocyte ratio( NLR) were lower, in addition, LYM, EOS counts and ratios, and NLR were all more effective in differentiating between the two pathogen infections, A combined analysis of these indicators further improved the differentiation efficacy. Therefore, LYM and EOS counts and ratios, along with NLR, can serve as effective blood parameters for differentiating Mycoplasma pneumoniae infections from influenza A virus infections in children.

Clinical Maternal and Neonatal Features in COVID-19 Infected Pregnancies in Tianjin, China

Purpose

Outbreak of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global pandemic, leading to over 6 million deaths worldwide. Pregnant women suffer from a higher risk facing the pandemic COVID-19, while their related clinical information is limited.

Methods

The clinical information of SARS-CoV-2 positive (n = 30) and negative pregnant women (n = 134) in Tianjin First Central Hospital (from November 30, 2022, to January 20, 2023) were collected. All statistical analyses were conducted in R language, employing t test or Chi-square test methods.

Results

Significantly higher heart rate, temperature, and intrapartum hemorrhage were observed in positive pregnant women, besides fetal placentation grading, umbilical cord around the neck, cardiac B-scan ultrasound, and ultrasonic examination of lower limb vessels were significantly differential between positive and negative individuals. As for coagulation test, significantly higher activated partial thromboplastin time (APTT), Thrombin Time (TT), and D-dimer (DD2) were found in SARS-CoV-2 positive patients. Liver function test results indicated that six indicators were significantly differential between positive and negative individuals.

Conclusion

Compared to negative pregnant women, significantly abnormal liver function and coagulopathy were observed in positive patients. As the unique vulnerable population, SARS-CoV-2 infected pregnant women should be payed more attention in clinical practice.

Circulating white blood cell traits and prolonged night shifts: a cross-sectional study based on nurses in Guangxi

This study aimed to elucidate the effects of long day and night shifts on immune cells in a population of nurses. This cross-sectional study in December 2019 was based on a group of nurses. 1568 physically healthy caregivers were included, including 1540 women and 28 men. 1093 nurses had long-term shift work (working in a rotating system for > 1 year). The receiver operating characteristic curve, Ensemble Learning, and Logistic regression analyses were used to evaluate factors related to long-term shift work. The night shift group nurses had significantly higher MPV, PLCR, and WBC and significantly lower BASO%, ELR, MCHC, PLR, RDW-CV, and RDW-SD (P < 0.01). ROC curves showed that WBC, PLR, ELR, RDW_CV, and BASO% were more related to the night shift. Ensemble Learning, combined with the LASSO model, finally filtered out three indicators of night shifts related to ELR, WBC, and RDW_SD. Finally, logistic regression analysis showed that the nurses' night shift situation greatly influenced two peripheral blood ELR and WBC indicators (ELR: log (OR) =  - 3.9, 95% CI: - 5.8- - 2.0; WBC: log (OR) = 0.25, 95% CI: 0.18-0.32). Finally, we showed that, unlike WBC, the relative riskiness of ELR showed opposite results among junior nurses and middle-senior nurses (log (OR) 6.5 (95% CI: 1.2, 13) and - 7.1 (95% CI: - 10, - 3.8), respectively). Our study found that prolonged night shifts were associated with abnormal WBC and ELR, but after strict age matching, WBC remained significantly different. These findings help to confirm that COVID-19 and tumorigenesis (e.g., breast cancer) are significantly associated with circadian rhythm disruption. However, more detailed studies are needed to confirm this.

The association between dental and dentoalveolar arch forms of children with normal occlusion and malocclusion: a cross-sectional study

BACKGROUND

Symmetrical and coordinated dental and alveolar arches are crucial for achieving proper occlusion. This study aimed to explore the association between dental and dentoalveolar arch forms in children with both normal occlusion and malocclusion.

METHODS

209 normal occlusion subjects (5-13 years, mean 8.48 years) and 199 malocclusion subjects (5-12 years, mean 8.19 years) were included. The dentoalveolar arch form was characterized by the smoothest projected curve representing the layered contour of the buccal alveolar bone, referred to as the LiLo curve. Subsequently, a polynomial function was utilized to assess dental and dentoalveolar arch forms. To facilitate separate analyses of shape (depth/width ratio) and size (depth and width), the widths of dental and dentoalveolar arch forms were normalized. The normalized dental and dentoalveolar arch forms (shapes) were further classified into 6 groups, termed dental/dentoalveolar arch clusters, using the k-means algorithm.

RESULTS

The association between dental and dentoalveolar arch clusters was found to be one-to-many rather than one-to-one. The mismatch between dental and dentoalveolar arch forms is common in malocclusion, affecting 11.4% of the maxilla and 9.2% of the mandible, respectively.

CONCLUSIONS

There are large individual variations in the association between dental and dentoalveolar arch forms. Early orthodontic treatment may play an active role in coordinating the relationship between the dental and dentoalveolar arch forms.

Multi-view progression diagnosis of thyroid cancer by integrating platelet transcriptomes and blood routine tests

Thyroid cancer is the most common type of endocrine system cancer. The pre-cancer and early stages are usually benign or slowly growing, and do not need invasive treatments. This study investigated the challenging classification task of four classes of samples, i.e., normal controls (N), thyroid adenomas (TA), papillary thyroid cancers (PTC) and metastasized papillary thyroid cancers (MPTC). We proposed a multi-view progression diagnosis framework ThyroidBloodTest to integrate the two views of RNAseq platelet transcriptomes (View-T) and blood routine (View-B) features. Platelet transcriptome represented the molecular-level information, while the blood routine features were easy to obtain in the clinical practice. Eleven feature selection algorithms and seven classifiers were evaluated for both views. The experimental data suggested the importance of choosing appropriate data analysis algorithms and feature engineering techniques like principal component analysis (PCA). The best ThyroidBloodTest model achieved Acc = 0.8750 for the four-class classification of the N/TA/PTC/MPTC samples based on the integrated feature space of View-T and View-B. The cellular localization cytosol and three post-translational modification types acetylation/phosphorylation/ubiquitination were observed to be enriched in the proteins encoded by the View-T biomarkers. The numbers of different immune cells also contributed positively to the progression diagnosis of thyroid cancer. The proposed multi-view prediction model demonstrated the necessity of integrating both platelet transcriptomes and blood routine tests for the progression diagnosis of thyroid cancer.

An interpretable machine learning framework for diagnosis and prognosis of COVID-19

Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k-nearest neighbors, isolated forests, and SMOTE to pre-process the original blood test datasets. Seven machine learning tools Support Vector Machine, Extra Tree, Random Forest, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, Logistic Regression, and ensemble learning were then used to diagnose and predict COVID-19. In addition, we used SHAP and scikit-learn post-hoc interpretability to report feature importance, allowing healthcare professionals and artificial intelligence models to interact to suggest biomarkers that some doctors may have missed. The 10-fold cross-validation of two public datasets shows that the performance of KISM is better than that of the current state-of-the-art methods. In the diagnostic COVID-19 task, an AUC value of 0.9869 and an accuracy of 0.9787 were obtained, and ultimately Leukocytes, platelets, and Proteina C reativa mg/dL were found to be the most indicative biomarkers for the diagnosis of COVID-19. An AUC value of 0.9949 and an accuracy of 0.9677 were obtained in the prognostic COVID-19 task and Age, LYMPH, and WBC were found to be the most indicative biomarkers for identifying the severity of the patient.

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic's main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID-19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.

Prediction of acute pancreatitis complications using routine blood parameters during early admission

BACKGROUND

There have been many reports on biomarkers for predicting the severity of acute pancreatitis (AP), but few studies on biomarkers for predicting complications; some simple and inexpensive indicators, in particular, are worth exploring.

METHODS

We retrospectively collected clinical data of 809 AP patients, including medical history and results of routine blood tests, and grouped them according to the occurrence of complications. Differences in clinical characteristics between groups with and without complications were compared using t-test or χ2 test. Receiver operating curve (ROC) and area under the curve were calculated to evaluate the ability of predicting the occurrence of complications for the routine blood parameters with statistical differences. Then, through univariate and multivariate analyses, independent risk factors closely associated with complications were identified. Finally, we built a three-parameter prediction system and evaluated its ability to predict AP complications.

RESULTS

Compared with the group without complications, the patients in the complication group had higher white blood cells, neutrophils, C-reactive protein, and erythrocyte sedimentation rate (ESR), and lower red blood cells and hemoglobin (Hb) (all p < .05), and most of them had severe pancreatitis. In addition, pseudocysts were more common in patients with alcoholic etiology, recurrence, low BMI, and high platelet (PLT) and plateletocrit. Acute respiratory failure was more common in patients with first onset and high mean PLT volume (MPV). Sepsis was more common in patients with lipogenic etiology, high MPV, and low lymphocytes. Infectious pancreatic necrosis was more common in patients with alcoholic etiology. Acute renal failure was more common in patients with monocytes and high MPV and low PLT. Multivariate analysis showed that PLT and ESR were risk factors for pseudocyst development. The ROC showed that the combination of Hb, PLT and ESR had a significantly higher predictive ability for pseudocyst than the single parameter.

CONCLUSION

Routine blood parameters can be used to predict the complications of AP. A predictive model combining ESR, PLT, and Hb may be an effective tool for identifying pseudocysts in AP patients.

Predictive models for COVID-19 detection using routine blood tests and machine learning

The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient's state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning.

Prognosing the risk of COVID-19 death through a machine learning-based routine blood panel: A retrospective study in Brazil

BACKGROUND

Despite an extensive network of primary care availability, Brazil has suffered profoundly during the COVID-19 pandemic, experiencing the greatest sanitary collapse in its history. Thus, it is important to understand phenotype risk factors for SARS-CoV-2 infection severity in the Brazilian population in order to provide novel insights into the pathogenesis of the disease.

OBJECTIVE

This study proposes to predict the risk of COVID-19 death through machine learning, using blood biomarkers data from patients admitted to two large hospitals in Brazil.

METHODS

We retrospectively collected blood biomarkers data in a 24-h time window from 6,979 patients with COVID-19 confirmed by positive RT-PCR admitted to two large hospitals in Brazil, of whom 291 (4.2%) died and 6,688 (95.8%) were discharged. We then developed a large-scale exploration of risk models to predict the probability of COVID-19 severity, finally choosing the best performing model regarding the average AUROC. To improve generalizability, for each model five different testing scenarios were conducted, including two external validations.

RESULTS

We developed a machine learning-based panel composed of parameters extracted from the complete blood count (lymphocytes, MCV, platelets and RDW), in addition to C-Reactive Protein, which yielded an average AUROC of 0.91 ± 0.01 to predict death by COVID-19 confirmed by positive RT-PCR within a 24-h window.

CONCLUSION

Our study suggests that routine laboratory variables could be useful to identify COVID-19 patients under higher risk of death using machine learning. Further studies are needed for validating the model in other populations and contexts, since the natural history of SARS-CoV-2 infection and its consequences on the hematopoietic system and other organs is still quite recent.

Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries

Objectives

The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice.

Methods

This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants.

Results

This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day.

Conclusions

This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.

Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network

Since February 2020, the world has been engaged in an intense struggle with the COVID-19 disease, and health systems have come under tragic pressure as the disease turned into a pandemic. The aim of this study is to obtain the most effective routine blood values (RBV) in the diagnosis and prognosis of COVID-19 using a backward feature elimination algorithm for the LogNNet reservoir neural network. The first dataset in the study consists of a total of 5296 patients with the same number of negative and positive COVID-19 tests. The LogNNet-model achieved the accuracy rate of 99.5% in the diagnosis of the disease with 46 features and the accuracy of 99.17% with only mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, and activated partial prothrombin time. The second dataset consists of a total of 3899 patients with a diagnosis of COVID-19 who were treated in hospital, of which 203 were severe patients and 3696 were mild patients. The model reached the accuracy rate of 94.4% in determining the prognosis of the disease with 48 features and the accuracy of 82.7% with only erythrocyte sedimentation rate, neutrophil count, and C reactive protein features. Our method will reduce the negative pressures on the health sector and help doctors to understand the pathogenesis of COVID-19 using the key features. The method is promising to create mobile health monitoring systems in the Internet of Things.

A Hybrid Data-Driven-Agent-Based Modelling Framework for Water Distribution Systems Contamination Response during COVID-19

Contamination events in water distribution systems (WDSs) are highly dangerous events in very vulnerable infrastructure where a quick response by water utility managers is indispensable. Various studies have explored methods to respond to water events and a variety of models have been developed to simulate the consequences and the reactions of all stakeholders involved. This study proposes a novel contamination response and recovery methodology using machine learning and knowledge of the topology and hydraulics of a water network inside of an agent-based model (ABM). An artificial neural network (ANN) is trained to predict the possible source of the contamination in the network, and the knowledge of the WDS and the possible flow directions throughout a demand pattern is utilized to verify that prediction. The utility manager agent can place mobile sensor equipment to trace the contamination spread after identifying the source to identify endangered and safe places in the water network and communicate that information to the consumer agents through water advisories. The contamination status of the network is continuously updated, and the consumers reaction and decision making are determined by a fuzzy logic system considering their social background, recent stress factors based on findings throughout the COVID-19 pandemic and their location in the network. The results indicate that the ANN-based support tool, paired with knowledge of the network, provides a promising support tool for utility managers to identify the source of a possible water event. The optimization of the ANN and the methodology led to accuracies up to 80%, depending on the number of sensors and the prediction types. Furthermore, the specified water advisories according to the mobile sensor placement provide the consumer agents with information on the contamination spread and urges them to seek for help or support less.

Development of a Machine Learning Based Web Application for Early Diagnosis of COVID-19 Based on Symptoms

Detecting the presence of a disease requires laboratory tests, testing kits, and devices; however, these were not always available on hand. This study proposes a new approach in disease detection using machine learning algorithms by analyzing symptoms experienced by a person without requiring laboratory tests. Six supervised machine learning algorithms such as J48 decision tree, random forest, support vector machine, k-nearest neighbors, naïve Bayes algorithms, and artificial neural networks were applied in the “COVID-19 Symptoms and Presence Dataset” from Kaggle. Through hyperparameter optimization and 10-fold cross validation, we attained the highest possible performance of each algorithm. A comparative analysis was performed according to accuracy, sensitivity, specificity, and area under the ROC curve. Results show that random forest, support vector machine, k-nearest neighbors, and artificial neural networks outweighed other algorithms by attaining 98.84% accuracy, 100% sensitivity, 98.79% specificity, and 98.84% area under the ROC curve. Finally, we developed a web application that will allow users to select symptoms currently being experienced, and use it to predict the presence of COVID-19 through the developed prediction model. Based on this mechanism, the proposed method can effectively predict the presence or absence of COVID-19 in a person immediately without using laboratory tests, kits, and devices in a real-time manner.

Challenges of Multiplex Assays for COVID-19 Research: A Machine Learning Perspective

Multiplex assays that provide simultaneous measurement of multiple analytes in biological samples have now developed into widely used technologies in the study of diseases, drug discovery, and other medical areas. These approaches span multiple assay systems and can provide readouts of specific assay components with similar accuracy as the respective single assay measurements. Multiplexing allows the consumption of lower sample volumes, lower costs, and higher throughput compared with carrying out single assays. A number of recent studies have demonstrated the impact of multiplex assays in the study of the SARS-CoV-2 virus, the infectious agent responsible for the current COVID-19 pandemic. In this respect, machine learning techniques have proven to be highly valuable in capturing complex disease phenotypes and converting these insights into models which can be applied in real-world settings. This chapter gives an overview of opportunities and challenges of multiplexed biomarker analysis, with a focus on the use of machine learning aimed at identification of biological signatures for increasing our understanding of COVID-19 disease, and for improved diagnostics and prediction of disease outcomes.

Neutrophil Extracellular Trap Formation Potential Correlates with Lung Disease Severity in COVID-19 Patients

Severe lung inflammation is common in life-threatening coronavirus disease 2019 (COVID-19). This study tested the hypothesis that polymorphonuclear (PMN, neutrophil) phenotype early in the course of disease progression would predict peak lung disease severity in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is increasingly evident that PMN activation contributes to tissue injury resulting from extracellular reactive oxygen species generation, granule exocytosis with release of proteases, neutrophil extracellular trap (NET) formation, and release of cytokines. The current study focuses on PMN activation in response to SARS-CoV-2 infection, specifically, the association between NETs and lung disease. This is a prospective cohort study at an academic medical center with patients enrolled within 4 days of admission at 3 tertiary hospitals: Clements University Hospital, Parkland Memorial Hospital, and Children’s Health in Dallas, TX. Patients were categorized as having minimal or moderate to severe lung disease based on peak respiratory support. Healthy donor controls matched for age, sex, race, and ethnicity were also enrolled. Neutrophils from COVID-19 patients displayed greater IL-8 expression, elastase release, and NET formation as compared with neutrophils from healthy donors. Importantly, neutrophils from COVID-19 patients had enhanced NET formation in the absence of any additional stimulus, not seen in PMN from healthy donors. Moreover, PMA-elicited NET formation by circulating PMN correlated with severity of lung disease. We speculate that neutrophil immuno-phenotyping can be used to predict lung disease severity in COVID-19 patients.

Incorporation of COVID-19-Inspired Behaviour into Agent-Based Modelling for Water Distribution Systems’ Contamination Responses

Drinking water contamination events in water networks are major challenges which require fast handling by the responsible water utility manager agent, and have been explored in a variety of models and scenarios using, e.g., agent-based modelling. This study proposes to use recent findings during the COVID-19 pandemic outbreak and draw analogies regarding responses and reactions to these kinds of challenges. This happens within an agent-based model coupled to a hydraulic simulation where the decision making of the individual agents is based on a fuzzy logic system reacting to a contamination event in a water network. Upon detection of anomalies in the water the utility manager agent places mobile sensor equipment in order to determine endangered areas in the water network and warn the consumer agents. Their actions are determined according to their social backgrounds, location in the water network and possible symptoms from ingesting contaminated water by utilising a fuzzy logic system. Results from an example application suggest that placing mobile equipment and warning consumers in real time is essential as part of a proper response to a contamination event. Furthermore, social background factors such as the age or employment status of the population can play a vital role in the consumer agents’ response to a water event.

COVID-19 Prediction Applying Supervised Machine Learning Algorithms with Comparative Analysis Using WEKA

Early diagnosis is crucial to prevent the development of a disease that may cause danger to human lives. COVID-19, which is a contagious disease that has mutated into several variants, has become a global pandemic that demands to be diagnosed as soon as possible. With the use of technology, available information concerning COVID-19 increases each day, and extracting useful information from massive data can be done through data mining. In this study, authors utilized several supervised machine learning algorithms in building a model to analyze and predict the presence of COVID-19 using the COVID-19 Symptoms and Presence dataset from Kaggle. J48 Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes algorithms were applied through WEKA machine learning software. Each model’s performance was evaluated using 10-fold cross validation and compared according to major accuracy measures, correctly or incorrectly classified instances, kappa, mean absolute error, and time taken to build the model. The results show that Support Vector Machine using Pearson VII universal kernel outweighs other algorithms by attaining 98.81% accuracy and a mean absolute error of 0.012.