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Comparative accuracy of prognostic models for short-term mortality in acute-on-chronic liver failure patients: CAP-ACLF

Verma N, Dhiman RK, Singh V, Duseja A, Taneja S, Choudhury A, et al.

Hepatol Int, 2021 Jun;15(3):753-765.
PMID: 34173167 DOI: 10.1007/s12072-021-10175-w

BACKGROUND: Multiple predictive models of mortality exist for acute-on-chronic liver failure (ACLF) patients that often create confusion during decision-making. We studied the natural history and evaluated the performance of prognostic models in ACLF patients.
METHODS: Prospectively collected data of ACLF patients from APASL-ACLF Research Consortium (AARC) was analyzed for 30-day outcomes. The models evaluated at days 0, 4, and 7 of presentation for 30-day mortality were: AARC (model and score), CLIF-C (ACLF score, and OF score), NACSELD-ACLF (model and binary), SOFA, APACHE-II, MELD, MELD-Lactate, and CTP. Evaluation parameters were discrimination (c-indices), calibration [accuracy, sensitivity, specificity, and positive/negative predictive values (PPV/NPV)], Akaike/Bayesian Information Criteria (AIC/BIC), Nagelkerke-R2, relative prediction errors, and odds ratios.
RESULTS: Thirty-day survival of the cohort (n = 2864) was 64.9% and was lowest for final-AARC-grade-III (32.8%) ACLF. Performance parameters of all models were best at day 7 than at day 4 or day 0 (p 12 had the lowest 30-day survival (5.7%).
CONCLUSIONS: APASL-ACLF is often a progressive disease, and models assessed up to day 7 of presentation reliably predict 30-day mortality. Day-7 AARC model is a statistically robust tool for classifying risk of death and accurately predicting 30-day outcomes with relatively lower prediction errors. Day-7 AARC score > 12 may be used as a futility criterion in APASL-ACLF patients.

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
On an Application of Sequential Bayesian Techniques for Estimating the Fatality Rate of Dengue in Malaysia

Kamarulzaman Bin Ibrahim, Abdul Aziz Jermain

Sains Malaysiana, 1997;26.

Dengue is one of the main factors of mortality of inhabitants in the region of South East Asia. Malaysia is one of the countries which is facing a high incidence of dengue, particularly in the 70's and early 80's. The Ministry of Health has taken various measures in order to reduce the dengue epidemic. These include educating people about dengue and conducting research such as investigation of factors that influence the epidemic of dengue. In this study, a sequential Bayesian approach is applied to data of the proportion of death due to dengue over the period from 1982 to 1992. In the sequential Bayesian approach, the data for the year 1982 becomes the prior information for the 1983 data and so on. The data for the different periods are combined in a chronological manner until the final posterior distribution of the proportion of death due to dengue is obtained. It is found that the overall proportion is 0.59% and its standard deviation is 0.00002%.
Denggi adalah satu daripada faktor utama kematian bagi penduduk di rantau Asia Tenggara. Malaysia pula merupakan satu daripada negara yang sedang mengalami kadar insiden denggi yang tinggi, khususnya dalam tahun 70-an dan pada awal 80-an. Kementerian Kesihatan telah mengambil pelbagai langkah untuk mengurangkan wabak denggi. Ini termasuk memberikan pendidikan tentang denggi dan membuat kajian tentang faktor-faktor yang mempengaruhinya. Dalam kajian ini kaedah Bayesan jujukan digunakan terhadap data perkadaran yang mati akibat denggi dalam tempoh 1982 hingga 1992. Dalam kaedah ini, data tahun 1982 digunakan sebagai maklumat prior untuk data tahun 1983 dan seterusnya. Data dari tahun yang berlainan digabungkan secara kronologi sehingga diperoleh taburan posterior yang terakhir bagi perkadaran yang mati akibat denggi. Didapati bahawa perkadaran keseluruhan ialah 0.59% dan sisihan piawainya 0.00002%.

Matched MeSH terms: Bayes Theorem
Prediction models for early risk detection of cardiovascular event

Purwanto, Eswaran C, Logeswaran R, Abdul Rahman AR

J Med Syst, 2012 Apr;36(2):521-31.
PMID: 22675726

Cardiovascular disease (CVD) is the major cause of death globally. More people die of CVDs each year than from any other disease. Over 80% of CVD deaths occur in low and middle income countries and occur almost equally in male and female. In this paper, different computational models based on Bayesian Networks, Multilayer Perceptron,Radial Basis Function and Logistic Regression methods are presented to predict early risk detection of the cardiovascular event. A total of 929 (626 male and 303 female) heart attack data are used to construct the models.The models are tested using combined as well as separate male and female data. Among the models used, it is found that the Multilayer Perceptron model yields the best accuracy result.

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 New Insights on the Evolution of the Sweet Taste Receptor of Primates Adapted to Harsh Environments

Tamrin NAM, Zainudin R, Esa Y, Alias H, Isa MNM, Croft L, et al.

Animals (Basel), 2020 Dec 10;10(12).
PMID: 33321745 DOI: 10.3390/ani10122359

Taste perception is an essential function that provides valuable dietary and sensory information, which is crucial for the survival of animals. Studies into the evolution of the sweet taste receptor gene (TAS1R2) are scarce, especially for Bornean endemic primates such as Nasalis larvatus (proboscis monkey), Pongo pygmaeus (Bornean orangutan), and Hylobates muelleri (Muller's Bornean gibbon). Primates are the perfect taxa to study as they are diverse dietary feeders, comprising specialist folivores, frugivores, gummivores, herbivores, and omnivores. We constructed phylogenetic trees of the TAS1R2 gene for 20 species of anthropoid primates using four different methods (neighbor-joining, maximum parsimony, maximum-likelihood, and Bayesian) and also established the time divergence of the phylogeny. The phylogeny successfully separated the primates into their taxonomic groups as well as by their dietary preferences. Of note, the reviewed time of divergence estimation for the primate speciation pattern in this study was more recent than the previously published estimates. It is believed that this difference may be due to environmental changes, such as food scarcity and climate change, during the late Miocene epoch, which forced primates to change their dietary preferences. These findings provide a starting point for further investigation.

Matched MeSH terms: Bayes Theorem
Fulltext Evolutionary pattern of Macaca fascicularis in Southeast Asia inferred using Y-chromosomal gene

Rovie-Ryan JJ, Khan FAA, Abdullah MT

BMC Ecol Evol, 2021 02 15;21(1):26.
PMID: 33588750 DOI: 10.1186/s12862-021-01757-1

BACKGROUND: We analyzed a combined segment (2032-bp) of the sex-determining region and the testis-specific protein of the Y-chromosome (Y-DNA) gene to clarify the gene flow and phylogenetic relationships of the long-tailed macaques (Macaca fascicularis) in Southeast Asia. Phylogenetic relationships were constructed using the maximum likelihood, Bayesian inference, and the median-joining network from a total of 164 adult male M. fascicularis from 62 localities in Malaysia, including sequences from the other regions from previous studies.
RESULTS: Based on Y-DNA, we confirm the presence of two lineages of M. fascicularis: the Indochinese and Sundaic lineages. The Indochinese lineage is represented by M. fascicularis located northwards of the Surat Thani-Krabi depression region and is introgressed by the Macaca mulatta Y-DNA. The Sundaic lineage is free from such hybridization event, thus defined as the original carrier of the M. fascicularis Y-DNA. We further revealed that the Sundaic lineage differentiated into two forms: the insular and the continental forms. The insular form, which represents the ancestral form of M. fascicularis, consists of two haplotypes: a single homogenous haplotype occupying the island of Borneo, Philippines, and southern Sumatra; and the Javan haplotype. The more diverse continental form consists of 17 haplotypes in which a dominant haplotype was shared by individuals from southern Thai Peninsular (south of Surat Thani-Krabi depression), Peninsular Malaysia, and Sumatra. Uniquely, Sumatra contains both the continental and insular Y-DNA which can be explained by a secondary contact hypothesis.
CONCLUSIONS: Overall, the findings in this study are important: (1) to help authority particularly in Malaysia on the population management activities including translocation and culling of conflict M. fascicularis, (2) to identify the unknown origin of captive M. fascicularis used in biomedical research, and; (3) the separation between the continental and insular forms warrants for the treatment as separate management units.

Matched MeSH terms: Bayes Theorem
Predicting enteric fever without bacteriological culture results

Ross IN, Abraham T

Trans R Soc Trop Med Hyg, 1987;81(3):374-7.
PMID: 3686631

We used Bayes' theorem to calculate the probability of enteric fever in 260 patients presenting with undiagnosed fever, without recourse to blood or stool culture results. These individuals were divided into 110 patients with enteric fever (63 culture positive, 47 culture negative) and 150 patients with other causes of fever. Comparison of the frequencies of occurrence of 19 clinical and laboratory events, said to be helpful in the diagnosis of enteric fever, in the two groups revealed that only 8 events were significantly more frequent in enteric fever. These were: a positive Widal test at a screening dilution of 1:40; a peak temperature greater than = 39 degrees C; previous treatment for the fever; a white blood cell count less than 9 X 10(6)/litre; a polymorphonuclear leucocyte count less than 3.5 X 10(6)/litre; splenomegaly; fever duration greater than 7 d; and hepatomegaly. When the probability of enteric fever was determined prospectively in 110 patients, using only 6 of these discriminating events, the probability of patients with a positive prediction having enteric fever (diagnostic specificity) was 0.80 (95% confidence interval: 0.68 to 0.91) and the probability of those with a negative prediction not having enteric fever (diagnostic sensitivity) was 0.92 (0.85 to 0.99). Using all 19 events did not alter the diagnostic specificity or diagnostic sensitivity. This study shows that a small number of clinical and laboratory features can objectively discriminate enteric fever from other causes of fever in the majority of patients. Calculating the probability of enteric fever can aid in diagnosis, when culturing for salmonella is either unavailable or is negative.

Matched MeSH terms: Bayes Theorem
Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals

Adam M, Oh SL, Sudarshan VK, Koh JE, Hagiwara Y, Tan JH, et al.

Comput Methods Programs Biomed, 2018 Jul;161:133-143.
PMID: 29852956 DOI: 10.1016/j.cmpb.2018.04.018

Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.

Matched MeSH terms: Bayes Theorem
Fulltext Bayesian inference of the Weibull model based on interval-censored survival data

Guure CB, Ibrahim NA, Adam MB

Comput Math Methods Med, 2013;2013:849520.
PMID: 23476718 DOI: 10.1155/2013/849520

Interval-censored data consist of adjacent inspection times that surround an unknown failure time. We have in this paper reviewed the classical approach which is maximum likelihood in estimating the Weibull parameters with interval-censored data. We have also considered the Bayesian approach in estimating the Weibull parameters with interval-censored data under three loss functions. This study became necessary because of the limited discussion in the literature, if at all, with regard to estimating the Weibull parameters with interval-censored data using Bayesian. A simulation study is carried out to compare the performances of the methods. A real data application is also illustrated. It has been observed from the study that the Bayesian estimator is preferred to the classical maximum likelihood estimator for both the scale and shape parameters.

Matched MeSH terms: Bayes Theorem*
Analysis of foveal avascular zone in colour fundus images for grading of diabetic retinopathy severity

Ahmad Fadzil M, Ngah NF, George TM, Izhar LI, Nugroho H, Adi Nugroho H

Annu Int Conf IEEE Eng Med Biol Soc, 2010;2010:5632-5.
PMID: 21097305 DOI: 10.1109/IEMBS.2010.5628041

Diabetic retinopathy (DR) is a sight threatening complication due to diabetes mellitus that affects the retina. At present, the classification of DR is based on the International Clinical Diabetic Retinopathy Disease Severity. In this paper, FAZ enlargement with DR progression is investigated to enable a new and an effective grading protocol DR severity in an observational clinical study. The performance of a computerised DR monitoring and grading system that digitally analyses colour fundus image to measure the enlargement of FAZ and grade DR is evaluated. The range of FAZ area is optimised to accurately determine DR severity stage and progression stages using a Gaussian Bayes classifier. The system achieves high accuracies of above 96%, sensitivities higher than 88% and specificities higher than 96%, in grading of DR severity. In particular, high sensitivity (100%), specificity (>98%) and accuracy (99%) values are obtained for No DR (normal) and Severe NPDR/PDR stages. The system performance indicates that the DR system is suitable for early detection of DR and for effective treatment of severe cases.

Matched MeSH terms: Bayes Theorem
Fulltext ECG-based prediction algorithm for imminent malignant ventricular arrhythmias using decision tree

Mandala S, Cai Di T, Sunar MS, Adiwijaya

PLoS One, 2020;15(5):e0231635.
PMID: 32407335 DOI: 10.1371/journal.pone.0231635

Spontaneous prediction of malignant ventricular arrhythmia (MVA) is useful to avoid delay in rescue operations. Recently, researchers have developed several algorithms to predict MVA using various features derived from electrocardiogram (ECG). However, there are several unresolved issues regarding MVA prediction such as the effect of number of ECG features on a prediction remaining unclear, possibility that an alert for occurring MVA may arrive very late and uncertainty in the performance of the algorithm predicting MVA minutes before onset. To overcome the aforementioned problems, this research conducts an in-depth study on the number and types of ECG features that are implemented in a decision tree classifier. In addition, this research also investigates an algorithm's execution time before the occurrence of MVA to minimize delays in warnings for MVA. Lastly, this research aims to study both the sensitivity and specificity of an algorithm to reveal the performance of MVA prediction algorithms from time to time. To strengthen the results of analysis, several classifiers such as support vector machine and naive Bayes are also examined for the purpose of comparison study. There are three phases required to achieve the objectives. The first phase is literature review on existing relevant studies. The second phase deals with design and development of four modules for predicting MVA. Rigorous experiments are performed in the feature selection and classification modules. The results show that eight ECG features with decision tree classifier achieved good prediction performance in terms of execution time and sensitivity. In addition, the results show that the highest percentage for sensitivity and specificity is 95% and 90% respectively, in the fourth 5-minute interval (15.1 minutes-20 minutes) that preceded the onset of an arrhythmia event. Such results imply that the fourth 5-minute interval would be the best time to perform prediction.

Matched MeSH terms: Bayes Theorem
Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms

He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, et al.

Sci Total Environ, 2019 May 01;663:1-15.
PMID: 30708212 DOI: 10.1016/j.scitotenv.2019.01.329

Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world.

Matched MeSH terms: Bayes Theorem
Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods

Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, et al.

Sci Total Environ, 2020 Jan 20;701:134979.
PMID: 31733400 DOI: 10.1016/j.scitotenv.2019.134979

Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets.

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*
Pemetaan kes kemortalan bayi di Semenanjung Malaysia menggunakan kaedah bayes empirik

Nuzlinda Abdul Rahman, Abdul Aziz Jemain, Kamarulzaman Ibrahim, Ahmad Mahir Razali

Sains Malaysiana, 2011;40.

Kajian ini bertujuan untuk memetakan kes kemortalan bayi mengikut daerah di Semenanjung Malaysia bagi tahun 1991 hingga 2000. Penganggaran risiko relatif berdasarkan kaedah Bayes empirik telah digunakan dalam kajian ini. Tiga kaedah penganggaran parameter dihuraikan iaitu kaedah momen, kaedah kebolehjadian maksimum dan kaedah penganggaran gabungan momen dan kebolehjadian maksimum. Keteguhan anggaran parameter yang diperoleh diuji menggunakan kaedah Bootstrap. Hasil kajian mendapati jurang antara kawasan berisiko rendah dengan kawasan berisiko tinggi adalah lebih besar pada awal dekad 2000 berbanding pada awal dekad 1990-an walaupun pada dasarnya kadar mortaliti bayi secara keseluruhannya adalah semakin berkurangan pada peringkat nasional. Kawasan pantai timur Semenanjung Malaysia masih pada takuk yang sama iaitu masih berada dalam kategori berisiko tinggi sepanjang tempoh yang dikaji. Seterusnya, gambaran terdapatnya tompokan risiko juga turut terpapar dalam peta yang dihasilkan. Berdasarkan kaedah Bootstrap, parameter-parameter yang dianggarkan dalam kajian ini adalah teguh.

Matched MeSH terms: Bayes Theorem
Fulltext A New Approach to Noninvasive-Prolonged Fatigue Identification Based on Surface EMG Time-Frequency and Wavelet Features

Jamaluddin FN, Ibrahim F, Ahmad SA

J Healthc Eng, 2023;2023:1951165.
PMID: 36756137 DOI: 10.1155/2023/1951165

In sports, fatigue management is vital as adequate rest builds strength and enhances performance, whereas inadequate rest exposes the body to prolonged fatigue (PF) or also known as overtraining. This paper presents PF identification and classification based on surface electromyography (EMG) signals. An experiment was performed on twenty participants to investigate the behaviour of surface EMG during the inception of PF. PF symptoms were induced in accord with a five-day Bruce Protocol treadmill test on four lower extremity muscles: the biceps femoris (BF), rectus femoris (RF), vastus medialis (VM), and vastus lateralis (VL). The results demonstrate that the experiment successfully induces soreness, unexplained lethargy, and performance decrement and also indicate that the progression of PF can be observed based on changes in frequency features (ΔF med and ΔF mean) and time features (ΔRMS and ΔMAV) of surface EMG. This study also demonstrates the ability of wavelet index features in PF identification. Using a naïve Bayes (NB) classifier exhibits the highest accuracy based on time and frequency features with 98% in distinguishing PF on RF, 94% on BF, 9% on VL, and 97% on VM. Thus, this study has positively indicated that surface EMG can be used in identifying the inception of PF. The implication of the findings is significant in sports to prevent a greater risk of PF.

Matched MeSH terms: Bayes Theorem
Fulltext Identifying risk factors for female cardiovascular disease patients in Malaysia: a bayesian approach

Nurliyana Juhan, Yong Zulina Zubairi, Zarina Mohd Khalid, Ahmad Syadi Mahmood Zuhdi

MATEMATIKA, 2018;34(101):15-23.
MyJurnal

Cardiovascular disease (CVD) includes coronary heart disease, cerebrovascular disease (stroke), peripheral artery disease, and atherosclerosis of the aorta. All females face the threat of CVD. But becoming aware of symptoms and signs is a great challenge since most adults at increased risk of cardiovascular disease (CVD) have no symptoms or obvious signs especially in females. The symptoms may be identified by the assessment of their risk factors. The Bayesian approach is a specific way in dealing with this kind of problem by formalizing a priori beliefs and of combining them with the available observations. This study aimed to identify associated risk factors in CVD among female patients presenting with ST Elevation Myocardial Infarction (STEMI) using Bayesian logistic regression and obtain a feasible model to describe the data. A total of 874 STEMI female patients in the National Cardiovascular Disease Database-Acute Coronary Syndrome (NCVD-ACS) registry year 2006-2013 were analysed. Bayesian Markov Chain Monte Carlo (MCMC) simulation approach was applied in the univariate and multivariate analysis. Model performance was assessed through the model calibration and discrimination. The final multivariate model of STEMI female patients consisted of six significant variables namely smoking, dyslipidaemia, myocardial infarction (MI), renal disease, Killip class and age group. Females aged 65 years and above have higher incidence of CVD and mortality is high among female patients with Killip class IV. Also, renal disease was a strong predictor of CVD mortality. Besides, performance measures for the model was considered good. Bayesian logistic regression model provided a better understanding on the associated risk factors of CVD for female patients which may help tailor prevention or treatment plans more effectively.

Matched MeSH terms: Bayes Theorem
Implementing relevance feedback in ligand-based virtual screening using Bayesian inference network

Abdo A, Salim N, Ahmed A

J Biomol Screen, 2011 Oct;16(9):1081-8.
PMID: 21862688 DOI: 10.1177/1087057111416658

Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.

Matched MeSH terms: Bayes Theorem
Modeling of passengers' safety perception for buses on mountainous roads

Khoo HL, Ahmed M

Accid Anal Prev, 2018 Apr;113:106-116.
PMID: 29407657 DOI: 10.1016/j.aap.2018.01.025

This study had developed a passenger safety perception model specifically for buses taking into consideration the various factors, namely driver characteristics, environmental conditions, and bus characteristics using Bayesian Network. The behaviour of bus driver is observed through the bus motion profile, measured in longitudinal, lateral, and vertical accelerations. The road geometry is recorded using GPS and is computed with the aid of the Google map while the perceived bus safety is rated by the passengers in the bus in real time. A total of 13 variables were derived and used in the model development. The developed Bayesian Network model shows that the type of bus and the experience of the driver on the investigated route could have an influence on passenger's perception of their safety on buses. Road geometry is an indirect influencing factor through the driver's behavior. The findings of this model are useful for the authorities to structure an effective strategy to improve the level of perceived bus safety. A high level of bus safety will definitely boost passenger usage confidence which will subsequently increase ridership.

Matched MeSH terms: Bayes Theorem

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