Displaying publications 41 - 60 of 311 in total

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  1. An empirical assessment of the closeness of hidden truncation and additive component based skewed distributions
    Hazarika PJ, Chakraborty S
    Sains Malaysiana, 2014;43:1801-1809.
    Hidden truncation (HT) and additive component (AC) are two well known paradigms of generating skewed distributions from known symmetric distribution. In case of normal distribution it has been known that both the above paradigms lead to Azzalini's (1985) skew normal distribution. While the HT directly gives the Azzalini's ( 1985) skew normal distribution, the one generated by AC also leads to the same distribution under a re parameterization proposed by Arnold and Gomez (2009). But no such re parameterization which leads to exactly the same distribution by these two paradigms has so far been suggested for the skewed distributions generated from symmetric logistic and Laplace distributions. In this article, an attempt has been made to investigate numerically as well as statistically the closeness of skew distributions generated by HT and AC methods under the same re parameterization of Arnold and Gomez (2009) in the case of logistic and Laplace distributions.
    Matched MeSH terms: Models, Statistical
  2. An ensemble model for forecasting infectious diseases in India
    Shashvat K, Basu R, Bhondekar PA, Kaur A
    Trop Biomed, 2019 Dec 01;36(4):822-832.
    PMID: 33597454
    Time series modelling and forecasting plays an important role in various domains. The objective of this paper is to construct a simple average ensemble method to forecast the number of cases for infectious diseases like dengue and typhoid and compare it by applying models for forecasting. In this paper we have also evaluated the correlation between the number of typhoid and dengue cases with the ecological variables. The monthly data of dengue and typhoid cases from 2014 to 2017 were taken from integrated diseases surveillance programme, Government of India. This data was analysed by three models namely support vector regression, neural network and linear regression. The proposed simple average ensemble model was constructed by ensemble of three applied regression models i.e. SVR, NN and LR. We combine the regression models based upon the error metrics such as Mean Square Error, Root Mean Square Error and Mean Absolute Error. It was found that proposed ensemble method performed better in terms of forecast measures. The finding demonstrates that the proposed model outperforms as compared to already available applied models on the basis of forecast accuracy.
    Matched MeSH terms: Models, Statistical
  3. Fulltext An innovations-based noise cancelling technique on inverse kepstrum whitening filter and adaptive FIR filter in beamforming structure
    Jeong J
    Sensors (Basel), 2011;11(7):6816-41.
    PMID: 22163987 DOI: 10.3390/s110706816
    This paper presents an acoustic noise cancelling technique using an inverse kepstrum system as an innovations-based whitening application for an adaptive finite impulse response (FIR) filter in beamforming structure. The inverse kepstrum method uses an innovations-whitened form from one acoustic path transfer function between a reference microphone sensor and a noise source so that the rear-end reference signal will then be a whitened sequence to a cascaded adaptive FIR filter in the beamforming structure. By using an inverse kepstrum filter as a whitening filter with the use of a delay filter, the cascaded adaptive FIR filter estimates only the numerator of the polynomial part from the ratio of overall combined transfer functions. The test results have shown that the adaptive FIR filter is more effective in beamforming structure than an adaptive noise cancelling (ANC) structure in terms of signal distortion in the desired signal and noise reduction in noise with nonminimum phase components. In addition, the inverse kepstrum method shows almost the same convergence level in estimate of noise statistics with the use of a smaller amount of adaptive FIR filter weights than the kepstrum method, hence it could provide better computational simplicity in processing. Furthermore, the rear-end inverse kepstrum method in beamforming structure has shown less signal distortion in the desired signal than the front-end kepstrum method and the front-end inverse kepstrum method in beamforming structure.
    Matched MeSH terms: Models, Statistical
  4. Fulltext An online time-to-event dashboard comparing the effective control of COVID-19 among continents using the inflection point on an ogive curve: Observational study
    Lee KW, Chien TW, Yeh YT, Chou W, Wang HY
    Medicine (Baltimore), 2021 Mar 12;100(10):e24749.
    PMID: 33725830 DOI: 10.1097/MD.0000000000024749
    BACKGROUND: During the COVID-19 pandemic, one of the frequently asked questions is which countries (or continents) are severely hit. Aside from using the number of confirmed cases and the fatality to measure the impact caused by COVID-19, few adopted the inflection point (IP) to represent the control capability of COVID-19. How to determine the IP days related to the capability is still unclear. This study aims to (i) build a predictive model based on item response theory (IRT) to determine the IP for countries, and (ii) compare which countries (or continents) are hit most.

    METHODS: We downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country.

    RESULTS: The top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61-0.86), 0.58 (0.31-0.84), and 0.54 (0.44-0.64), respectively. An online time-event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents.

    CONCLUSION: An IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP.

    Matched MeSH terms: Models, Statistical*
  5. Fulltext An overview of health forecasting
    Soyiri IN, Reidpath DD
    Environ Health Prev Med, 2013 Jan;18(1):1-9.
    PMID: 22949173 DOI: 10.1007/s12199-012-0294-6
    Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery.
    Matched MeSH terms: Models, Statistical
  6. Ancestry-informative marker (AIM) SNP panel for the Malay population
    Yahya P, Sulong S, Harun A, Wangkumhang P, Wilantho A, Ngamphiw C, et al.
    Int J Legal Med, 2020 Jan;134(1):123-134.
    PMID: 31760471 DOI: 10.1007/s00414-019-02184-0
    Ancestry-informative markers (AIMs) can be used to infer the ancestry of an individual to minimize the inaccuracy of self-reported ethnicity in biomedical research. In this study, we describe three methods for selecting AIM SNPs for the Malay population (Malay AIM panel) using different approaches based on pairwise FST, informativeness for assignment (In), and PCA-correlated SNPs (PCAIMs). These Malay AIM panels were extracted from genotype data stored in SNP arrays hosted by the Malaysian node of the Human Variome Project (MyHVP) and the Singapore Genome Variation Project (SGVP). In particular, genotype data from a total of 165 Malay individuals were analyzed, comprising data on 117 individual genotypes from the Affymetrix SNP-6 SNP array platform and data on 48 individual genotypes from the OMNI 2.5 Illumina SNP array platform. The HapMap phase 3 database (1397 individuals from 11 populations) was used as a reference for comparison with the Malay genotype data. The accuracy of each resulting Malay AIM panel was evaluated using a machine learning "ancestry-predictive model" constructed by using WEKA, a comprehensive machine learning platform written in Java. A total of 1250 SNPs were finally selected, which successfully identified Malay individuals from other world populations with an accuracy of 90%, but the accuracy decreased to 80% using 157 SNPs according to the pairwise FST method, while a panel of 200 SNPs selected using In and PCAIMs could be used to identify Malay individuals with an accuracy of approximately 80%.
    Matched MeSH terms: Models, Statistical
  7. Application of Box-Behnken design for ultrasound-assisted extraction and recycling preparative HPLC for isolation of anthraquinones from Cassia singueana
    Jibril S, Basar N, Sirat HM, Wahab RA, Mahat NA, Nahar L, et al.
    Phytochem Anal, 2019 Jan;30(1):101-109.
    PMID: 30288828 DOI: 10.1002/pca.2795
    INTRODUCTION: Cassia singueana Del. (Fabaceae) is a rare medicinal plant used in the traditional medicine preparations to treat various ailments. The root of C. singueana is a rich source of anthraquinones that possess anticancer, antibacterial and antifungal properties.

    OBJECTIVE: The objective of this study was to develop an ultrasound-assisted extraction (UAE) method for achieving a high extraction yield of anthraquinones using the response surface methodology (RSM), Box-Behnken design (BBD), and a recycling preparative high-performance liquid chromatography (HPLC) protocol for isolation of anthraquinones from C. singueana.

    METHODOLOGY: Optimisation of UAE was performed using the Box-Behnken experimental design. Recycling preparative HPLC was employed to isolate anthraquinones from the root extract of C. singueana.

    RESULTS: The BBD was well-described by a quadratic polynomial model (R2  = 0.9751). The predicted optimal UAE conditions for a high extraction yield were obtained at: extraction time 25.00 min, temperature 50°C and solvent-sample ratio of 10 mL/g. Under the predicted conditions, the experimental value (1.65 ± 0.07%) closely agreed to the predicted yield (1.64%). The obtained crude extract of C. singueana root was subsequently purified to afford eight anthraquinones.

    CONCLUSION: The extraction protocol described here is suitable for large-scale extraction of anthraquinones from plant extracts.

    Matched MeSH terms: Models, Statistical
  8. Fulltext Application of an Effective Statistical Technique for an Accurate and Powerful Mining of Quantitative Trait Loci for Rice Aroma Trait
    Golestan Hashemi FS, Rafii MY, Ismail MR, Mohamed MT, Rahim HA, Latif MA, et al.
    PLoS One, 2015;10(6):e0129069.
    PMID: 26061689 DOI: 10.1371/journal.pone.0129069
    When a phenotype of interest is associated with an external/internal covariate, covariate inclusion in quantitative trait loci (QTL) analyses can diminish residual variation and subsequently enhance the ability of QTL detection. In the in vitro synthesis of 2-acetyl-1-pyrroline (2AP), the main fragrance compound in rice, the thermal processing during the Maillard-type reaction between proline and carbohydrate reduction produces a roasted, popcorn-like aroma. Hence, for the first time, we included the proline amino acid, an important precursor of 2AP, as a covariate in our QTL mapping analyses to precisely explore the genetic factors affecting natural variation for rice scent. Consequently, two QTLs were traced on chromosomes 4 and 8. They explained from 20% to 49% of the total aroma phenotypic variance. Additionally, by saturating the interval harboring the major QTL using gene-based primers, a putative allele of fgr (major genetic determinant of fragrance) was mapped in the QTL on the 8th chromosome in the interval RM223-SCU015RM (1.63 cM). These loci supported previous studies of different accessions. Such QTLs can be widely used by breeders in crop improvement programs and for further fine mapping. Moreover, no previous studies and findings were found on simultaneous assessment of the relationship among 2AP, proline and fragrance QTLs. Therefore, our findings can help further our understanding of the metabolomic and genetic basis of 2AP biosynthesis in aromatic rice.
    Matched MeSH terms: Models, Statistical
  9. Fulltext Application of phase congruency for discriminating some lung diseases using chest radiograph
    Rijal OM, Ebrahimian H, Noor NM, Hussin A, Yunus A, Mahayiddin AA
    Comput Math Methods Med, 2015;2015:424970.
    PMID: 25918551 DOI: 10.1155/2015/424970
    A novel procedure using phase congruency is proposed for discriminating some lung disease using chest radiograph. Phase congruency provides information about transitions between adjacent pixels. Abrupt changes of phase congruency values between pixels may suggest a possible boundary or another feature that may be used for discrimination. This property of phase congruency may have potential for deciding between disease present and disease absent where the regions of infection on the images have no obvious shape, size, or configuration. Five texture measures calculated from phase congruency and Gabor were shown to be normally distributed. This gave good indicators of discrimination errors in the form of the probability of Type I Error (δ) and the probability of Type II Error (β). However, since 1 -  δ is the true positive fraction (TPF) and β is the false positive fraction (FPF), an ROC analysis was used to decide on the choice of texture measures. Given that features are normally distributed, for the discrimination between disease present and disease absent, energy, contrast, and homogeneity from phase congruency gave better results compared to those using Gabor. Similarly, for the more difficult problem of discriminating lobar pneumonia and lung cancer, entropy and homogeneity from phase congruency gave better results relative to Gabor.
    Matched MeSH terms: Models, Statistical
  10. Fulltext Application of statistical experimental designs for optimization of medium composition in biodegradation of procion red MX-8B
    Khadijah, O., Lee, K.K., Abdullah, M.F.F.
    ASM Science Journal, 2010;4(2):103-112.
    MyJurnal
    Two sequential statistical experimental designs were used to screen and investigate the dependence of the amount of biodegradation of Procion Red MX-8B (PR-MX8B) on the fermentation variables. Fourteen factors were screened using the Plackett-Burman design. Among these factors, the most significant variables which included yeast extract, corn steep solids and starch influencing PR-MX8B decolourisation were statistically elucidated for optimization. The optimum concentrations of 5.00 g/l yeast extract, 2.99 g/l starch and 1.89 g/l corn steep solids were predicted by applying the Box-Behnken design to the second order polynomial model fitted to the results obtained. The best predicted optimal conditions verified experimentally yielded 72.11% while the predicted value from the polynomial model was 79.17%. The experimental values were in good agreement with the predicted values with a 90.81% degree of accuracy.
    Matched MeSH terms: Models, Statistical
  11. Application of statistical experimental methodology to optimize bioremediation of n-alkanes in aquatic environment
    Zahed MA, Aziz HA, Mohajeri L, Mohajeri S, Kutty SR, Isa MH
    J Hazard Mater, 2010 Dec 15;184(1-3):350-6.
    PMID: 20837377 DOI: 10.1016/j.jhazmat.2010.08.043
    Response surface methodology (RSM) was employed to optimize nitrogen and phosphorus concentrations for removal of n-alkanes from crude oil contaminated seawater samples in batch reactors. Erlenmeyer flasks were used as bioreactors; each containing 250 mL dispersed crude oil contaminated seawater, indigenous acclimatized microorganism and different amounts of nitrogen and phosphorus based on central composite design (CCD). Samples were extracted and analyzed according to US-EPA protocols using a gas chromatograph. During 28 days of bioremediation, a maximum of 95% total aliphatic hydrocarbons removal was observed. The obtained Model F-value of 267.73 and probability F<0.0001 implied the model was significant. Numerical condition optimization via a quadratic model, predicted 98% n-alkanes removal for a 20-day laboratory bioremediation trial using nitrogen and phosphorus concentrations of 13.62 and 1.39 mg/L, respectively. In actual experiments, 95% removal was observed under these conditions.
    Matched MeSH terms: Models, Statistical
  12. Application of the central composite design for condition optimization for semi-aerobic landfill leachate treatment using electrochemical oxidation
    Mohajeri S, Aziz HA, Isa MH, Zahed MA, Bashir MJ, Adlan MN
    Water Sci Technol, 2010;61(5):1257-66.
    PMID: 20220248 DOI: 10.2166/wst.2010.018
    In the present study, Electrochemical Oxidation was used to remove COD and color from semi-aerobic landfill leachate collected from Pulau Burung Landfill Site (PBLS), Penang, Malaysia. Experiments were conducted in a batch laboratory-scale system in the presence of NaCl as electrolyte and aluminum electrodes. Central composite design (CCD) under Response surface methodology (RSM) was applied to optimize the electrochemical oxidation process conditions using chemical oxygen demand (COD) and color removals as responses, and the electrolyte concentrations, current density and reaction time as control factors. Analysis of variance (ANOVA) showed good coefficient of determination (R(2)) values of >0.98, thus ensuring satisfactory fitting of the second-order regression model with the experimental data. In un-optimized condition, maximum removals for COD (48.77%) and color (58.21%) were achieved at current density 80 mA/cm(2), electrolyte concentration 3,000 mg/L and reaction time 240 min. While after optimization at current density 75 mA/cm(2), electrolyte concentration 2,000 mg/L and reaction time 218 min a maximum of 49.33 and 59.24% removals were observed for COD and color respectively.
    Matched MeSH terms: Models, Statistical
  13. Application of the threshold model for modelling and forecasting of exchange rate in selected ASEAN countries
    Behrooz Gharleghi, Abu Hassan Shaari Md Nor, Tamat Sarmidi
    Sains Malaysiana, 2014;43:1609-1622.
    Linear time series models are not able to capture the behaviour of many financial time series, as in the cases of exchange rates and stock market data. Some phenomena, such as volatility and structural breaks in time series data, cannot be modelled implicitly using linear time series models. Therefore, nonlinear time series models are typically designed to accommodate for such nonlinear features. In the present study, a nonlinearity test and a structural change test are used to detect the nonlinearity and the break date in three ASEAN currencies, namely the Indonesian Rupiah (IDR), the Malaysian Ringgit (MYR) and the Thai Baht (THB). The study finds that the null hypothesis of linearity is rejected and evidence of structural breaks exist in the exchange rates series. Therefore, the decision to use the self-exciting threshold autoregressive (SETAR) model in the present study is justified. The results showed that the SETAR model, as a regime switching model, can explain abrupt changes in a time series. To evaluate the prediction performance of SETAR model, an Autoregressive Integrated Moving Average (ARIMA) model used as a benchmark. In order to increase the accuracy of prediction, both models are combined with an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model. The prediction results showed that the construct model of SETAR-EGARCH performs better than that of the ARIMA model and the combined ARIMA and EGARCH model. The results indicated that nonlinear models give better fitting than linear models.
    Matched MeSH terms: Models, Statistical
  14. Applying a random parameters Negative Binomial Lindley model to examine multi-vehicle crashes along rural mountainous highways in Malaysia
    Rusli R, Haque MM, Afghari AP, King M
    Accid Anal Prev, 2018 Oct;119:80-90.
    PMID: 30007211 DOI: 10.1016/j.aap.2018.07.006
    Road safety in rural mountainous areas is a major concern as mountainous highways represent a complex road traffic environment due to complex topology and extreme weather conditions and are associated with more severe crashes compared to crashes along roads in flatter areas. The use of crash modelling to identify crash contributing factors along rural mountainous highways suffers from limitations in data availability, particularly in developing countries like Malaysia, and related challenges due to the presence of excess zero observations. To address these challenges, the objective of this study was to develop a safety performance function for multi-vehicle crashes along rural mountainous highways in Malaysia. To overcome the data limitations, an in-depth field survey, in addition to utilization of secondary data sources, was carried out to collect relevant information including roadway geometric factors, traffic characteristics, real-time weather conditions, cross-sectional elements, roadside features, and spatial characteristics. To address heterogeneity resulting from excess zeros, three specialized modelling techniques for excess zeros including Random Parameters Negative Binomial (RPNB), Random Parameters Negative Binomial - Lindley (RPNB-L) and Random Parameters Negative Binomial - Generalized Exponential (RPNB-GE) were employed. Results showed that the RPNB-L model outperformed the other two models in terms of prediction ability and model fit. It was found that heavy rainfall at the time of crash and the presence of minor junctions along mountainous highways increase the likelihood of multi-vehicle crashes, while the presence of horizontal curves along a steep gradient, the presence of a passing lane and presence of road delineation decrease the likelihood of multi-vehicle crashes. Findings of this study have significant implications for road safety along rural mountainous highways, particularly in the context of developing countries.
    Matched MeSH terms: Models, Statistical
  15. Appropriateness of deep vein thrombosis (DVT) prophylaxis use among medical inpatients: a DVT risk alert tool (DRAT) study
    Diana Yap FS, Ng ZY, Wong CY, Muhamad Saifuzzaman MK, Yang LB
    Med J Malaysia, 2019 02;74(1):45-50.
    PMID: 30846662
    INTRODUCTION: Increasing incidence of Venous Thromboembolism (VTE) has complicated treatment courses for hospitalised patients. Despite recommendation to support deep vein thrombosis (DVT) risk assessment and appropriate use of prophylaxis in medical inpatients, it is either neglected or prescribed unnecessarily by the clinicians. This study aimed to assess and compare the appropriateness of DVT prophylaxis prescribing between usual care versus a pharmacist-driven DVT Risk Alert Tool (DRAT) intervention among hospitalised medical patients.

    METHODS: A prospective pre- and post-intervention study was conducted among medical inpatients in a Malaysian secondary care hospital. DVT and bleeding risks were stratified using validated Padua Risk Assessment Model (RAM) and International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) Bleeding Risk Assessment Model. Pharmacist-driven DRAT was developed and implemented post-interventional phase. DVT prophylaxis use was determined and its appropriateness was compared between pre and post study using multivariate logistic regression with IBM SPSS software version 21.0.

    RESULTS: Overall, 286 patients (n=142 pre-intervention versus n=144 post-intervention) were conveniently recruited. The prevalence of DVT prophylaxis use was 10.8%. Appropriate thromboprophylaxis prescribing increased from 64.8% to 68.1% post-DRAT implementation. Of note, among high DVT risk patients, DRAT intervention was observed to be a significant predictor of appropriate thromboprophylaxis use (14.3% versus 31.3%; adjusted odds ratio=2.80; 95% CI 1.01 to 7.80; p<0.05).

    CONCLUSION: The appropriateness of DVT prophylaxis use was suboptimal but doubled after implementation of DRAT intervention. Thus, an integrated risk stratification checklist is an effective approach for the improvement of rational DVT prophylaxis use.

    Matched MeSH terms: Models, Statistical
  16. Assessing doctors' competence: application of CUSUM technique in monitoring doctors' performance
    Lim TO, Soraya A, Ding LM, Morad Z
    Int J Qual Health Care, 2002 Jun;14(3):251-8.
    PMID: 12108535
    Quality assurance of medical practice requires assessment of doctors' performance, whether informally via a system such as peer review or more formally via one such as credentialing. Current methods of assessment are, however, subjective or implicit. More objective methods of assessment based on statistical process control technique such as cumulative sum (CUSUM) procedure may be helpful.
    Matched MeSH terms: Models, Statistical
  17. Fulltext Assessment of land use factors associated with dengue cases in Malaysia using Boosted Regression Trees
    Cheong YL, Leitão PJ, Lakes T
    Spat Spatiotemporal Epidemiol, 2014 Jul;10:75-84.
    PMID: 25113593 DOI: 10.1016/j.sste.2014.05.002
    The transmission of dengue disease is influenced by complex interactions among vector, host and virus. Land use such as water bodies or certain agricultural practices have been identified as likely risk factors for dengue because of the provision of suitable habitats for the vector. Many studies have focused on the land use factors of dengue vector abundance in small areas but have not yet studied the relationship between land use factors and dengue cases for large regions. This study aims to clarify if land use factors other than human settlements, e.g. different types of agricultural land use, water bodies and forest are associated with reported dengue cases from 2008 to 2010 in the state of Selangor, Malaysia. From the correlative relationship, we aim to generate a prediction risk map. We used Boosted Regression Trees (BRT) to account for nonlinearities and interactions between the factors with high predictive accuracies. Our model with a cross-validated performance score (Area Under the Receiver Operator Characteristic Curve, ROC AUC) of 0.81 showed that the most important land use factors are human settlements (model importance of 39.2%), followed by water bodies (16.1%), mixed horticulture (8.7%), open land (7.5%) and neglected grassland (6.7%). A risk map after 100 model runs with a cross-validated ROC AUC mean of 0.81 (±0.001 s.d.) is presented. Our findings may be an important asset for improving surveillance and control interventions for dengue.
    Matched MeSH terms: Models, Statistical*
  18. Assessment of the likelihood of the introduction of foot-and-mouth disease through importation of live animals into the Malaysia-Thailand-Myanmar peninsula
    Wongsathapornchai K, Salman MD, Edwards JR, Morley PS, Keefe TJ, Van Campen H, et al.
    Am J Vet Res, 2008 Feb;69(2):252-60.
    PMID: 18241023 DOI: 10.2460/ajvr.69.2.252
    To assess the likelihood of an introduction of foot-and-mouth disease (FMD) into the Malaysia-Thailand-Myanmar (MTM) peninsula through terrestrial movement of livestock.
    Matched MeSH terms: Models, Statistical
  19. Automated characterization of fatty liver disease and cirrhosis using curvelet transform and entropy features extracted from ultrasound images
    Acharya UR, Raghavendra U, Fujita H, Hagiwara Y, Koh JE, Jen Hong T, et al.
    Comput Biol Med, 2016 12 01;79:250-258.
    PMID: 27825038 DOI: 10.1016/j.compbiomed.2016.10.022
    Fatty liver disease (FLD) is reversible disease and can be treated, if it is identified at an early stage. However, if diagnosed at the later stage, it can progress to an advanced liver disease such as cirrhosis which may ultimately lead to death. Therefore, it is essential to detect it at an early stage before the disease progresses to an irreversible stage. Several non-invasive computer-aided techniques are proposed to assist in the early detection of FLD and cirrhosis using ultrasound images. In this work, we are proposing an algorithm to discriminate automatically the normal, FLD and cirrhosis ultrasound images using curvelet transform (CT) method. Higher order spectra (HOS) bispectrum, HOS phase, fuzzy, Kapoor, max, Renyi, Shannon, Vajda and Yager entropies are extracted from CT coefficients. These extracted features are subjected to locality sensitive discriminant analysis (LSDA) feature reduction method. Then these LSDA coefficients ranked based on F-value are fed to different classifiers to choose the best performing classifier using minimum number of features. Our proposed technique can characterize normal, FLD and cirrhosis using probabilistic neural network (PNN) classifier with an accuracy of 97.33%, specificity of 100.00% and sensitivity of 96.00% using only six features. In addition, these chosen features are used to develop a liver disease index (LDI) to differentiate the normal, FLD and cirrhosis classes using a single number. This can significantly help the radiologists to discriminate FLD and cirrhosis in their routine liver screening.
    Matched MeSH terms: Models, Statistical
  20. Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images
    Mookiah MR, Acharya UR, Koh JE, Chandran V, Chua CK, Tan JH, et al.
    Comput Biol Med, 2014 Oct;53:55-64.
    PMID: 25127409 DOI: 10.1016/j.compbiomed.2014.07.015
    Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback-Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.
    Matched MeSH terms: Models, Statistical
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