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Fulltext Testing students' e-learning via Facebook through Bayesian structural equation modeling

Salarzadeh Jenatabadi H, Moghavvemi S, Wan Mohamed Radzi CWJB, Babashamsi P, Arashi M

PLoS One, 2017;12(9):e0182311.
PMID: 28886019 DOI: 10.1371/journal.pone.0182311

Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

Matched MeSH terms: Bayes Theorem*
Tacrolimus Therapy in Adult Heart Transplant Recipients: Evaluation of a Bayesian Forecasting Software

Kirubakaran R, Stocker SL, Carlos L, Day RO, Carland JE

Ther Drug Monit, 2021 Dec 01;43(6):736-746.
PMID: 34126624 DOI: 10.1097/FTD.0000000000000909

BACKGROUND: Therapeutic drug monitoring is recommended to guide tacrolimus dosing because of its narrow therapeutic window and considerable pharmacokinetic variability. This study assessed tacrolimus dosing and monitoring practices in heart transplant recipients and evaluated the predictive performance of a Bayesian forecasting software using a renal transplant-derived tacrolimus model to predict tacrolimus concentrations.
METHODS: A retrospective audit of heart transplant recipients (n = 87) treated with tacrolimus was performed. Relevant data were collected from the time of transplant to discharge. The concordance of tacrolimus dosing and monitoring according to hospital guidelines was assessed. The observed and software-predicted tacrolimus concentrations (n = 931) were compared for the first 3 weeks of oral immediate-release tacrolimus (Prograf) therapy, and the predictive performance (bias and imprecision) of the software was evaluated.
RESULTS: The majority (96%) of initial oral tacrolimus doses were guideline concordant. Most initial intravenous doses (93%) were lower than the guideline recommendations. Overall, 36% of initial tacrolimus doses were administered to transplant recipients with an estimated glomerular filtration rate of <60 mL/min/1.73 m despite recommendations to delay the commencement of therapy. Of the tacrolimus concentrations collected during oral therapy (n = 1498), 25% were trough concentrations obtained at steady-state. The software displayed acceptable predictions of tacrolimus concentration from day 12 (bias: -6%; 95%confidence interval, -11.8 to 2.5; imprecision: 16%; 95% confidence interval, 8.7-24.3) of therapy.
CONCLUSIONS: Tacrolimus dosing and monitoring were discordant with the guidelines. The Bayesian forecasting software was suitable for guiding tacrolimus dosing after 11 days of therapy in heart transplant recipients. Understanding the factors contributing to the variability in tacrolimus pharmacokinetics immediately after transplant may help improve software predictions.

Matched MeSH terms: Bayes Theorem
Systematic placement of an enigmatic Southeast Asian taxon Eupetes macrocerus and implications for the biogeography of a main songbird radiation, the Passerida

Jønsson KA, Fjeldså J, Ericson PG, Irestedt M

Biol Lett, 2007 Jun 22;3(3):323-6.
PMID: 17347105

Biogeographic connections between Australia and other continents are still poorly understood although the plate tectonics of the Indo-Pacific region is now well described. Eupetes macrocerus is an enigmatic taxon distributed in a small area on the Malay Peninsula and on Sumatra and Borneo. It has generally been associated with Ptilorrhoa in New Guinea on the other side of Wallace's Line, but a relationship with the West African Picathartes has also been suggested. Using three nuclear markers, we demonstrate that Eupetes is the sister taxon of the South African genus Chaetops, and their sister taxon in turn being Picathartes, with a divergence in the Eocene. Thus, this clade is distributed in remote corners of Africa and Asia, which makes the biogeographic history of these birds very intriguing. The most parsimonious explanation would be that they represent a relictual basal group in the Passerida clade established after a long-distance dispersal from the Australo-Papuan region to Africa. Many earlier taxonomic arrangements may have been based on assumptions about relationships with similar-looking forms in the same, or adjacent, biogeographic regions, and revisions with molecular data may uncover such cases of neglect of ancient relictual patterns reflecting past connections between the continents.

Matched MeSH terms: Bayes Theorem
Fulltext Students' learning style detection using tree augmented naive Bayes

Li LX, Abdul Rahman SS

R Soc Open Sci, 2018 Jul;5(7):172108.
PMID: 30109052 DOI: 10.1098/rsos.172108

Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity. Past researches have proposed various approaches to detect the students' learning styles. Among all, the Bayesian network has emerged as a widely used method to automatically detect students' learning styles. On the other hand, tree augmented naive Bayesian network has the ability to improve the naive Bayesian network in terms of better classification accuracy. In this paper, we evaluate the performance of the tree augmented naive Bayesian in automatically detecting students' learning style in the online learning environment. The experimental results are promising as the tree augmented naive Bayes network is shown to achieve higher detection accuracy when compared to the Bayesian network.

Matched MeSH terms: Bayes Theorem
Fulltext Stroke Care Trends During COVID-19 Pandemic in Zanjan Province, Iran. From the CASCADE Initiative: Statistical Analysis Plan and Preliminary Results

Ghoreishi A, Arsang-Jang S, Sabaa-Ayoun Z, Yassi N, Sylaja PN, Akbari Y, et al.

J Stroke Cerebrovasc Dis, 2020 Dec;29(12):105321.
PMID: 33069086 DOI: 10.1016/j.jstrokecerebrovasdis.2020.105321

BACKGROUND: The emergence of the COVID-19 pandemic has significantly impacted global healthcare systems and this may affect stroke care and outcomes. This study examines the changes in stroke epidemiology and care during the COVID-19 pandemic in Zanjan Province, Iran.
METHODS: This study is part of the CASCADE international initiative. From February 18, 2019, to July 18, 2020, we followed ischemic and hemorrhagic stroke hospitalization rates and outcomes in Valiasr Hospital, Zanjan, Iran. We used a Bayesian hierarchical model and an interrupted time series analysis (ITS) to identify changes in stroke hospitalization rate, baseline stroke severity [measured by the National Institutes of Health Stroke Scale (NIHSS)], disability [measured by the modified Rankin Scale (mRS)], presentation time (last seen normal to hospital presentation), thrombolytic therapy rate, median door-to-needle time, length of hospital stay, and in-hospital mortality. We compared in-hospital mortality between study periods using Cox-regression model.
RESULTS: During the study period, 1,026 stroke patients were hospitalized. Stroke hospitalization rates per 100,000 population decreased from 68.09 before the pandemic to 44.50 during the pandemic, with a significant decline in both Bayesian [Beta: -1.034; Standard Error (SE): 0.22, 95% CrI: -1.48, -0.59] and ITS analysis (estimate: -1.03, SE = 0.24, p

Matched MeSH terms: Bayes Theorem
Spirobranchus bakau sp. nov. from Singapore: yet another species of S. kraussii-complex (Polychaeta: Serpulidae)

Sivananthan GD, Shantti P, Kupriyanova EK, Quek ZBR, Yap NWL, Teo SLM

Zootaxa, 2021 Sep 20;5040(1):33-65.
PMID: 34811055 DOI: 10.11646/zootaxa.5040.1.2

The intertidal serpulid polychaete Spirobranchus kraussii was originally described from South Africa and has since been reported in numerous sub (tropical) localities around the world. Recently, however, S. kraussii was uncovered as a complex of morphologically similar and geographically restricted species, raising the need to revise S. cf. kraussii populations. We formally describe S. cf. kraussii from Singapore mangroves as Spirobranchus bakau sp. nov. based on morphological and molecular data. Despite their morphological similarities, Maximum Likelihood and Bayesian Inference analyses of 18S and Cyt b DNA sequence data confirm that S. bakau sp. nov. is genetically distinct from S. kraussii and other known species in the complex. Both analyses recovered S. bakau sp. nov. as part of a strongly supported clade (96% bootstrap, 1 posterior probability), comprising S. sinuspersicus, S. kraussii and S. cf. kraussii from Australia and Hawaii. Additionally, paratypes of S. kraussii var. manilensis, described from Manila Bay in the Philippines, were examined and elevated to the full species S. manilensis. Finally, we tested the hypothesis that fertilisation and embryonic development of S. bakau sp. nov. can occur under the wide range of salinities (19.630.9 psu) and temperatures (2531C) reported in the Johor Strait. Fertilisation success of ≥70% was achieved across a temperature range of 2532C and a salinity range of 2032 psu. Embryonic development, however, had a narrower salinity tolerance range of 2732 psu. Clarifying the taxonomic status of S. cf. kraussii populations reported from localities elsewhere in Singapore and Southeast Asia will be useful in establishing the geographical distribution of S. bakau sp. nov. and other members of the S. kraussii-complex.

Matched MeSH terms: Bayes Theorem
Spatial analysis of infant mortality in peninsular malaysia over three decades using mixture models

Nuzlinda Abdul Rahman, Abdul Aziz Jemain

Sains Malaysiana, 2013;42:1003-1010.

Infant mortality is one of the central public issues in most of the developing countries. In Malaysia, the infant mortality rates have improved at the national level over the last few decades. However, the issue concerned is whether the improvement is uniformly distributed throughout the country. The aim of this study was to investigate the geographical distribution of infant mortality in Peninsular Malaysia from the year 1970 to 2000 using a technique known as disease mapping. It is assumed that the random variable of infant mortality cases comes from Poisson distribution. Mixture models were used to find the number of optimum components/groups for infant mortality data for every district in Peninsular Malaysia. Every component is assumed to have the same distribution, but different parameters. The number of optimum components were obtained by maximum likelihood approach via the EM algorithm. Bayes theorem was used to determine the probability of belonging to each district in every components of the mixture distribution. Each district was assigned to the component that had the highest posterior probability of belonging. The results obtained were visually presented in maps. The analysis showed that in the early year of 1970, the spatial heterogeneity effect was more prominent; however, towards the end of 1990, this pattern tended to disappear. The reduction in the spatial heterogeneity effect in infant mortality data indicated that the provisions of health services throughout the Peninsular Malaysia have improved over the period of the study, particularly towards the year 2000.

Matched MeSH terms: Bayes Theorem
Smoothing a discrete hazard function for the number of patients colonized with Methicillin-resistant Staphylococcus Aureus in an intensive care unit

Ismail NA, Pettitt AN

Stat Med, 2004 Apr 30;23(8):1247-58.
PMID: 15083481

A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.

Matched MeSH terms: Bayes Theorem
Fulltext Smart Helmet 5.0 for Industrial Internet of Things Using Artificial Intelligence

Campero-Jurado I, Márquez-Sánchez S, Quintanar-Gómez J, Rodríguez S, Corchado JM

Sensors (Basel), 2020 Nov 01;20(21).
PMID: 33139608 DOI: 10.3390/s20216241

Information and communication technologies (ICTs) have contributed to advances in Occupational Health and Safety, improving the security of workers. The use of Personal Protective Equipment (PPE) based on ICTs reduces the risk of accidents in the workplace, thanks to the capacity of the equipment to make decisions on the basis of environmental factors. Paradigms such as the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) make it possible to generate PPE models feasibly and create devices with more advanced characteristics such as monitoring, sensing the environment and risk detection between others. The working environment is monitored continuously by these models and they notify the employees and their supervisors of any anomalies and threats. This paper presents a smart helmet prototype that monitors the conditions in the workers' environment and performs a near real-time evaluation of risks. The data collected by sensors is sent to an AI-driven platform for analysis. The training dataset consisted of 11,755 samples and 12 different scenarios. As part of this research, a comparative study of the state-of-the-art models of supervised learning is carried out. Moreover, the use of a Deep Convolutional Neural Network (ConvNet/CNN) is proposed for the detection of possible occupational risks. The data are processed to make them suitable for the CNN and the results are compared against a Static Neural Network (NN), Naive Bayes Classifier (NB) and Support Vector Machine (SVM), where the CNN had an accuracy of 92.05% in cross-validation.

Matched MeSH terms: Bayes Theorem
Similarity-based virtual screening with a bayesian inference network

Abdo A, Salim N

ChemMedChem, 2009 Feb;4(2):210-8.
PMID: 19072820 DOI: 10.1002/cmdc.200800290

Many methods have been developed to capture the biological similarity between two compounds for use in drug discovery. A variety of similarity metrics have been introduced, the Tanimoto coefficient being the most prominent. Many of the approaches assume that molecular features or descriptors that do not relate to the biological activity carry the same weight as the important aspects in terms of biological similarity. Herein, a novel similarity searching approach using a Bayesian inference network is discussed. Similarity searching is regarded as an inference or evidential reasoning process in which the probability that a given compound has biological similarity with the query is estimated and used as evidence. Our experiments demonstrate that the similarity approach based on Bayesian inference networks is likely to outperform the Tanimoto similarity search and offer a promising alternative to existing similarity search approaches.

Matched MeSH terms: Bayes Theorem*
Fulltext Similarity measure and domain adaptation in multiple mixture model clustering: An application to image processing

Leong SH, Ong SH

PLoS One, 2017;12(7):e0180307.
PMID: 28686634 DOI: 10.1371/journal.pone.0180307

This paper considers three crucial issues in processing scaled down image, the representation of partial image, similarity measure and domain adaptation. Two Gaussian mixture model based algorithms are proposed to effectively preserve image details and avoids image degradation. Multiple partial images are clustered separately through Gaussian mixture model clustering with a scan and select procedure to enhance the inclusion of small image details. The local image features, represented by maximum likelihood estimates of the mixture components, are classified by using the modified Bayes factor (MBF) as a similarity measure. The detection of novel local features from MBF will suggest domain adaptation, which is changing the number of components of the Gaussian mixture model. The performance of the proposed algorithms are evaluated with simulated data and real images and it is shown to perform much better than existing Gaussian mixture model based algorithms in reproducing images with higher structural similarity index.

Matched MeSH terms: Bayes Theorem
Fulltext Signature selection forces and evolutionary divergence of immune-survival genes compared between two important shrimp species

Soo TCC, Bhassu S

PLoS One, 2023;18(1):e0280250.
PMID: 36634148 DOI: 10.1371/journal.pone.0280250

In recent years, shrimp aquaculture industry had grown significantly to become the major source of global shrimp production. Despite that, shrimp aquaculture production was impeded by various shrimp diseases over the past decades. Interestingly, different shrimp species demonstrated variable levels of immune strength and survival (immune-survival) ability towards different diseases, especially the much stronger immune-survival ability shown by the ancient shrimp species, Macrobrachium rosenbergii compared to other shrimp species. In this study, two important shrimp species, M. rosenbergii and Penaeus monodon (disease tolerant strain) (uninfected control and VpAHPND-infected) were compared to uncover the potential underlying genetic factors. The shrimp species were sampled, followed by RNA extraction and cDNA conversion. Five important immune-survival genes (C-type Lectin, HMGB, STAT, ALF3, and ATPase 8/6) were selected for PCR, sequencing, and subsequent genetics analysis. The overall genetic analyses conducted, including Analysis of Molecular Variance (AMOVA) and population differentiation, showed significant genetic differentiation (p<0.05) between different genes of M. rosenbergii and P. monodon. There was greater genetic divergence identified between HMGB subgroups of P. monodon (uninfected control and VpAHPND-infected) compared to other genes. Besides that, based on neutrality tests conducted, purifying selection was determined to be the main evolutionary driving force of M. rosenbergii and P. monodon with stronger purifying selection exhibited in M. rosenbergii genes. Potential balancing selection was identified for VpAHPND-infected HMGB subgroup whereas directional selection was detected for HMGB (both species) and ATPase 8/6 (only P. monodon) genes. The divergence times between M. rosenbergii and P. monodon genes were estimated through Bayesian molecular clock analysis, which were 438.6 mya (C-type Lectin), 1885.4 mya (HMGB), 432.6 mya (STAT), 448.1 mya (ALF3), and 426.4 mya (ATPase 8/6) respectively. In conclusion, important selection forces and evolutionary divergence information of immune-survival genes between M. rosenbergii and P. monodon were successfully identified.

Matched MeSH terms: Bayes Theorem
Fulltext Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

Nhu VH, Shirzadi A, Shahabi H, Singh SK, Al-Ansari N, Clague JJ, et al.

Int J Environ Res Public Health, 2020 04 16;17(8).
PMID: 32316191 DOI: 10.3390/ijerph17082749

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms-Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine-in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

Matched MeSH terms: Bayes Theorem*
Fulltext Sexual dimorphism in shell size of the land snail Leptopoma perlucidum (Caenogastropoda: Cyclophoridae)

Phung CC, Choo MH, Liew TS

PeerJ, 2022;10:e13501.
PMID: 35651743 DOI: 10.7717/peerj.13501

Sexual dimorphism in the shell size and shape of land snails has been less explored compared to that of other marine and freshwater snail taxa. This study examined the differences in shell size and shape across both sexes of Leptopoma perlucidum land snails. We collected 84 land snails of both sexes from two isolated populations on two islands off Borneo. A total of five shell size variables were measured: (1) shell height, (2) shell width, (3) shell spire height, (4) aperture height, and (5) aperture width. We performed frequentist and Bayesian t-tests to determine if there was a significant difference between the two sexes of L. perlucidum on each of the five shell measurements. Additionally, the shell shape was quantified based on nine landmark points using the geometric morphometric approach. We used generalised Procrustes and principal component analyses to test the effects of sex and location on shell shape. The results showed that female shells were larger than male shells across all five measurements (all with p-values < 0.05), but particularly in regards to shell height and shell width. Future taxonomic studies looking to resolve the Leptopoma species' status should consider the variability of shell size caused by sexual dimorphism.

Matched MeSH terms: Bayes Theorem
Fulltext SENTIMENT ANALYSIS OF PEOPLE’S ACCEPTANCE TOWARDS THE NEW MALAYSIAN GOVERNMENT USING NAÏVE BAYES METHOD

R.U GOBITHAASAN, NUR FARHANA SYAHIRA CHE HAMID

UMT Journal of Undergraduate Research, 2020;2(3):93-102.
MyJurnal

Sentiment analysis is a field of research that has a significant impact on today’s nations, politics and businesses. It is an algorithmic process to comprehend the opinions of a given subject based on the Natural Language Processing (NLP) methodologies. It has received much attention in recent years and is proven vital in various fields, e.g., online product reviews and social media analysis (Twitter, Facebook, etc.). This paper reports the outcome of sentiment analysis to investigate people’s acceptance of Pakatan Harapan, as the new Malaysian government, spearheaded by Tun Dr. Mahathir Mohamad and Dr. Wan Azizah, with an influence of Dato Seri Anwar Ibrahim. The objective is to classify tweets into three types of sentiments; positive, neutral and negative using Naïve Bayes method which is readily available in Python. The first step is tweets extraction for a month (March to April 2019) using search queries: {Pakatan Harapan, Mahathir, Anwar Ibrahim, Wan Azizah}. It is followed by tweets wrangling using NLP library and lastly output visualization in the form of a word cloud. A word cloud is a visual representation of text data with various font sizes depending on its probabilities. Final results showed that the tweets related to new government consist of neutral sentiment (41%) followed by positive sentiment (30%) and negative sentiment (29%). Malaysians do prefer the new government. However careful mitigation steps must be crafted to overcome controversial issues such as the ‘Rome Statute’ to avoid negative digital footprint, hence winning the Malaysians’ heart.

Matched MeSH terms: Bayes Theorem
Fulltext Robust observation detection for single object tracking: deterministic and probabilistic patch-based approaches

Zulkifley MA, Rawlinson D, Moran B

Sensors (Basel), 2012;12(11):15638-70.
PMID: 23202226 DOI: 10.3390/s121115638

In video analytics, robust observation detection is very important as the content of the videos varies a lot, especially for tracking implementation. Contrary to the image processing field, the problems of blurring, moderate deformation, low illumination surroundings, illumination change and homogenous texture are normally encountered in video analytics. Patch-Based Observation Detection (PBOD) is developed to improve detection robustness to complex scenes by fusing both feature- and template-based recognition methods. While we believe that feature-based detectors are more distinctive,however, for finding the matching between the frames are best achieved by a collection of points as in template-based detectors. Two methods of PBOD-the deterministic and probabilistic approaches-have been tested to find the best mode of detection. Both algorithms start by building comparison vectors at each detected points of interest. The vectors are matched to build candidate patches based on their respective coordination. For the deterministic method, patch matching is done in 2-level test where threshold-based position and size smoothing are applied to the patch with the highest correlation value. Forthe second approach, patch matching is done probabilistically by modelling the histograms of the patches by Poisson distributions for both RGB and HSV colour models. Then,maximum likelihood is applied for position smoothing while a Bayesian approach is appliedfor size smoothing. The result showed that probabilistic PBOD outperforms the deterministic approach with average distance error of 10.03% compared with 21.03%. This algorithm is best implemented as a complement to other simpler detection methods due to heavy processing requirement.

Matched MeSH terms: Bayes Theorem
Fulltext Robust identification of polyethylene terephthalate (PET) plastics through Bayesian decision

Zulkifley MA, Mustafa MM, Hussain A, Mustapha A, Ramli S

PLoS One, 2014;9(12):e114518.
PMID: 25485630 DOI: 10.1371/journal.pone.0114518

Recycling is one of the most efficient methods for environmental friendly waste management. Among municipal wastes, plastics are the most common material that can be easily recycled and polyethylene terephthalate (PET) is one of its major types. PET material is used in consumer goods packaging such as drinking bottles, toiletry containers, food packaging and many more. Usually, a recycling process is tailored to a specific material for optimal purification and decontamination to obtain high grade recyclable material. The quantity and quality of the sorting process are limited by the capacity of human workers that suffer from fatigue and boredom. Several automated sorting systems have been proposed in the literature that include using chemical, proximity and vision sensors. The main advantages of vision based sensors are its environmentally friendly approach, non-intrusive detection and capability of high throughput. However, the existing methods rely heavily on deterministic approaches that make them less accurate as the variations in PET plastic waste appearance are too high. We proposed a probabilistic approach of modeling the PET material by analyzing the reflection region and its surrounding. Three parameters are modeled by Gaussian and exponential distributions: color, size and distance of the reflection region. The final classification is made through a supervised training method of likelihood ratio test. The main novelty of the proposed method is the probabilistic approach in integrating various PET material signatures that are contaminated by stains under constant lighting changes. The system is evaluated by using four performance metrics: precision, recall, accuracy and error. Our system performed the best in all evaluation metrics compared to the benchmark methods. The system can be further improved by fusing all neighborhood information in decision making and by implementing the system in a graphics processing unit for faster processing speed.

Matched MeSH terms: Bayes Theorem*
Fulltext Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset

M. Hafiz Fazren Abd Rahman, Wan Wardatul Amani Wan Salim, M. Firdaus Abd-Wahab

Biological and Natural Resources Engineering Journal, 2020;3(1):48-61.
MyJurnal

The steep rise of cases pertaining to Diabetes Mellitus (DM) condition among global population has encouraged extensive researches on DM, which led to exhaustive accumulation of data related to DM. In this case, data mining and machine learning applications prove to be a powerful tool in transforming data into meaningful deductions. Several machine learning tools have shown great promise in diabetes classification. However, challenges remain in obtaining an accurate model suitable for real world application. Most disease risk-prediction modelling are found to be specific to a local population. Moreover, real-world data are likely to be complex, incomplete and unorganized, thus, convoluting efforts to develop models around it. This research aims to develop a robust prediction model for classification of type 2 diabetes mellitus (T2DM), with the interest of a Malaysian population, using three different machine learning algorithms; Decision Tree, Support Vector Machine and Naïve Bayes. Data pre-processing methods are utilised to the raw data to improve model performance. This study uses datasets obtained from the IIUM Medical Centre for classification and modelling. Ultimately, the performance of each model is validated, evaluated and compared based on several statistical metrics that measures accuracy, precision, sensitivity and efficiency. This study shows that the random forest model provides the best overall prediction performance in terms of accuracy (0.87), sensitivity (0.9), specificity (0.8), precision (0.9), F1-score (0.9) and AUC value (0.93) (Normal).

Matched MeSH terms: Bayes Theorem
Fulltext Risk Factors of Mortality among Male Patients with Cardiovascular Disease in Malaysia Using Bayesian Analysis

Juhan N, Zubairi YZ, Khalid ZM, Mahmood Zuhdi AS

Iran J Public Health, 2020 Sep;49(9):1642-1649.
PMID: 33643938 DOI: 10.18502/ijph.v49i9.4080

Background: Identifying risk factors associated with mortality is important in providing better prognosis to patients. Consistent with that, Bayesian approach offers a great advantage where it rests on the assumption that all model parameters are random quantities and hence can incorporate prior knowledge. Therefore, we aimed to develop a reliable model to identify risk factors associated with mortality among ST-Elevation Myocardial Infarction (STEMI) male patients using Bayesian approach.
Methods: A total of 7180 STEMI male patients from the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry for the years 2006-2013 were enrolled. In the development of univariate and multivariate logistic regression model for the STEMI patients, Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied. The performance of the model was assessed through convergence diagnostics, overall model fit, model calibration and discrimination.
Results: A set of six risk factors for cardiovascular death among STEMI male patients were identified from the Bayesian multivariate logistic model namely age, diabetes mellitus, family history of CVD, Killip class, chronic lung disease and renal disease respectively. Overall model fit, model calibration and discrimination were considered good for the proposed model.
Conclusion: Bayesian risk prediction model for CVD male patients identified six risk factors associated with mortality. Among the highest risks were Killip class (OR=18.0), renal disease (2.46) and age group (OR=2.43) respectively.

Matched MeSH terms: Bayes Theorem
Retinal image analysis for disease screening through local tetra patterns

Porwal P, Pachade S, Kokare M, Giancardo L, Mériaudeau F

Comput Biol Med, 2018 11 01;102:200-210.
PMID: 30308336 DOI: 10.1016/j.compbiomed.2018.09.028

Age-related Macular Degeneration (AMD) and Diabetic Retinopathy (DR) are the most prevalent diseases responsible for visual impairment in the world. This work investigates discrimination potential in the texture of color fundus images to distinguish between diseased and healthy cases by avoiding the prior lesion segmentation step. It presents a retinal background characterization approach and explores the potential of Local Tetra Patterns (LTrP) for texture classification of AMD, DR and Normal images. Five different experiments distinguishing between DR - normal, AMD - normal, DR - AMD, pathological - normal and AMD - DR - normal cases were conducted and validated using the proposed approach, and promising results were obtained. For all five experiments, different classifiers namely, AdaBoost, c4.5, logistic regression, naive Bayes, neural network, random forest and support vector machine were tested. We experimented with three public datasets, ARIA, STARE and E-Optha. Further, the performance of LTrP is compared with other texture descriptors, such as local phase quantization, local binary pattern and local derivative pattern. In all cases, the proposed method obtained the area under the receiver operating characteristic curve and f-score values higher than 0.78 and 0.746 respectively. It was found that both performance measures achieve over 0.995 for DR and AMD detection using a random forest classifier. The obtained results suggest that the proposed technique can discriminate retinal disease using texture information and has potential to be an important component for an automated screening solution for retinal images.

Matched MeSH terms: Bayes Theorem

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