Displaying publications 61 - 80 of 252 in total

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  1. Psychosocial rehabilitation models in the Asia-Pacific region
    Deva MP
    Psychiatry Clin Neurosci, 1998 Dec;52 Suppl:S364-6.
    PMID: 9895195
    Mental illnesses and mental health have, out of ignorance and fear, evoked a low priority in healthcare systems the world over. The concept that all mental illnesses were synonymous with madness has its origins in the beginnings of ignorance and fear. To a large extent, these have contributed to the marginalization of psychiatry and neglect of the mentally ill. The kings of old, seeing the ill-treatment of the mentally ill, built asylums for them, but again, the prejudice soon was overwhelming and care of the mentally ill was often given to those who were not the best administrators and carers. The long and controversial tradition of mental asylum care for the mentally ill was also brought about by the lack of specific treatments for mental illnesses. With the advent of chlorpromazine in the 1950s and other psychotropics afterwards, the need for incarceration in asylums became largely redundant. However, what also became obvious soon after was the fact that the psychotropics only helped to control symptoms and not to cure diseases of the mind. Although considerable research has gone into attempts at correcting supposed defects in neurotransmission, the cure of mental illness seems some way off. The need for rehabilitating or re-housing those with mental illnesses especially those needing long-term care and those whose cure seems difficult has been recognized for a long time. It was Phillipe Pinel who almost 200 years ago unchained the mental patients at an asylum in Paris and proposed work therapy and humane care. Psychosocial rehabilitation of the mentally ill is not, therefore, a new concept. The need for methods of changing the behavior, thinking and functioning of those with severe mental illnesses using psychological, social, occupational, behavioral and medical methods is called psychosocial rehabilitation. This method, although developed in stages over the past two centuries, has undergone changes and deterioration and development in different parts of the world with different priorities and emphasis.
    Matched MeSH terms: Forecasting
  2. Formation, modeling and validation of trihalomethanes (THM) in Malaysian drinking water: a case study in the districts of Tampin, Negeri Sembilan and Sabak Bernam, Selangor, Malaysia
    Abdullah MP, Yew CH, Ramli MS
    Water Res, 2003 Nov;37(19):4637-44.
    PMID: 14568050
    A modeling procedure that predicts trihalomethane (THM) formation from field sampling at the treatment plant and along its distribution system using Tampin district, Negeri Sembilan and Sabak Bernam district, Selangor as sources of data were studied and developed. Using Pearson method of correlation, the organic matter measured as TOC showed a positive correlation with formation of THM (r=0.380,P=0.0001 for Tampin and r=0.478,P=0.0001 for Sabak Bernam). Similar positive correlation was also obtained for pH in both districts with Tampin (r=0.362,P=0.0010) and Sabak Bernam (r=0.215,P=0.0010). Chlorine dosage was also found to have low correlation with formation of THM for the two districts with Tampin (r=0.233,P=0.0230) and Sabak Bernam (r=0.505,P=0.0001). Distance from treatment plant was found to have correlation with formation of THM for Tampin district with r=0.353 and P=0.0010. Other parameters such as turbidity, ammonia, temperature and residue chlorine were found to have no correlation with formation of THM. Linear and non-linear models were developed for these two districts. The results obtained were validated using three different sets of field data obtained from own source and district of Seremban (Pantai and Sg. Terip), Negeri Sembilan. Validation results indicated that there was significant difference in the predictive and determined values of THM when two sets of data from districts of Seremban were used with an exception of field data of Sg. Terip for non-linear model developed for district of Tampin. It was found that a non-linear model is slightly better than linear model in terms of percentage prediction errors. The models developed were site specific and the predictive capabilities in the distribution systems vary with different environmental conditions.
    Matched MeSH terms: Forecasting
  3. Fracture risk prediction in post-menopausal women with osteopenia and osteoporosis: preliminary findings
    Nik Mohd Hatta NNK, Lokman M, Said N M, Daud A, Ibrahim M, Sharifudin MA, et al.
    Enferm Clin, 2018 Feb;28 Suppl 1:232-235.
    PMID: 29650194 DOI: 10.1016/S1130-8621(18)30074-3
    OBJECTIVE: The study aims to identify the risk of obtaining a fracture among post-menopausal women with osteopenia and osteoporosis.

    METHOD: This work was a cross-sectional study involving a purposive sample of 87 post-menopausal women who attended the orthopedic and menopause clinics of Hospital Tengku Ampuan Afzan, Kuantan. The data were entered into the WHO fracture risk assessment tool (FRAX®) to predict major fracture and risk for hip fracture in 10 years' time.

    RESULTS: The mean age of the respondents was 61.6 years (SD=7.9). Among the respondents, 50.6% had osteopenia and nearly half (48.3%) had osteoporosis. The mean number of menopausal years of the respondents was 11.9 (SD=8.5), ranging between 1 and 44 years. The FRAX findings indicated 9.7% major osteoporotic fracture probability and 3.5% hip fracture probability, which were denoted as high risk. A Pearson correlation coefficient was computed to assess the relationship between menopausal years and the FRAX major osteoporotic fracture probability. A significant positive correlation was found between the two, but the correlation was weak (r=0.581, n=87, p < 0.001).

    CONCLUSIONS: The present findings indicate that menopausal years have a positive correlation with the risk of obtaining a fracture.

    Study site: orthopedic and menopause clinics of Hospital Tengku Ampuan Afzan, Kuantan.
    Matched MeSH terms: Forecasting
  4. A Report on Ten Asia Pacific Countries on Current Status and Future Directions of the Genetic Counseling Profession: The Establishment of the Professional Society of Genetic Counselors in Asia
    Laurino MY, Leppig KA, Abad PJ, Cham B, Chu YWY, Kejriwal S, et al.
    J Genet Couns, 2018 02;27(1):21-32.
    PMID: 28699126 DOI: 10.1007/s10897-017-0115-6
    The Professional Society of Genetic Counselors in Asia (PSGCA) was recently established as a special interest group of the Asia Pacific Society of Human Genetics. Fostering partnerships across the globe, the PSGCA's vision is to be the lead organization that advances and mainstreams the genetic counseling profession in Asia and ensures individuals have access to genetic counseling services. Its mission is to promote quality genetic counseling services in the region by enhancing practice and curricular standards, research and continuing education. The PSGCA was formally launched during the Genetic Counseling Pre-Conference Workshop held at the 11th Asia-Pacific Conference on Human Genetics in Hanoi, Viet Nam, September 16, 2015. The pre-conference workshop provided an opportunity for medical geneticists and genetic counselors from across 10 Asia Pacific countries to learn about the varied genetic counseling practices and strategies for genetic counseling training. This paper provides an overview of the current status and challenges in these countries, and proposed course of unified actions for the future of the genetic counseling profession.
    Matched MeSH terms: Forecasting
  5. Energy consumption and CO2 emissions in Iran, 2025
    Mirzaei M, Bekri M
    Environ Res, 2017 Apr;154:345-351.
    PMID: 28161426 DOI: 10.1016/j.envres.2017.01.023
    Climate change and global warming as the key human societies' threats are essentially associated with energy consumption and CO2 emissions. A system dynamic model was developed in this study to model the energy consumption and CO2 emission trends for Iran over 2000-2025. Energy policy factors are considered in analyzing the impact of different energy consumption factors on environmental quality. The simulation results show that the total energy consumption is predicted to reach 2150 by 2025, while that value in 2010 is 1910, which increased by 4.3% yearly. Accordingly, the total CO2 emissions in 2025 will reach 985million tonnes, which shows about 5% increase yearly. Furthermore, we constructed policy scenarios based on energy intensity reduction. The analysis show that CO2 emissions will decrease by 12.14% in 2025 compared to 2010 in the scenario of 5% energy intensity reduction, and 17.8% in the 10% energy intensity reduction scenario. The results obtained in this study provide substantial awareness regarding Irans future energy and CO2 emission outlines.
    Matched MeSH terms: Forecasting
  6. Prospective Advances in Circular RNA Investigation
    Sulaiman SA, Abdul Murad NA, Mohamad Hanif EA, Abu N, Jamal R
    Adv Exp Med Biol, 2018 9 28;1087:357-370.
    PMID: 30259380 DOI: 10.1007/978-981-13-1426-1_28
    circRNAs have emerged as one of the key regulators in many cellular mechanisms and pathogenesis of diseases. However, with the limited knowledge and current technologies for circRNA investigations, there are several challenges that need to be addressed for. These include challenges in understanding the regulation of circRNA biogenesis, experimental designs, and sample preparations to characterize the circRNAs in diseases as well as the bioinformatics pipelines and algorithms. In this chapter, we discussed the above challenges and possible strategies to overcome those limitations. We also addressed the differences between the existing applications and technologies to study the circRNAs in diseases. By addressing these challenges, further understanding of circRNAs roles and regulations as well as the discovery of novel circRNAs could be achieved.
    Matched MeSH terms: Forecasting
  7. Projections of the Healthcare Costs and Disease Burden due to Hepatitis C Infection under Different Treatment Policies in Malaysia, 2018-2040
    McDonald SA, Azzeri A, Shabaruddin FH, Dahlui M, Tan SS, Kamarulzaman A, et al.
    Appl Health Econ Health Policy, 2018 12;16(6):847-857.
    PMID: 30145775 DOI: 10.1007/s40258-018-0425-3
    INTRODUCTION: The World Health Organisation (WHO) has set ambitious goals to reduce the global disease burden associated with, and eventually eliminate, viral hepatitis.

    OBJECTIVE: To assist with achieving these goals and to inform the development of a national strategic plan for Malaysia, we estimated the long-term burden incurred by the care and management of patients with chronic hepatitis C virus (HCV) infection. We compared cumulative healthcare costs and disease burden under different treatment cascade scenarios.

    METHODS: We attached direct costs for the management/care of chronically HCV-infected patients to a previously developed clinical disease progression model. Under assumptions regarding disease stage-specific proportions of model-predicted HCV patients within care, annual numbers of patients initiated on antiviral treatment and distribution of treatments over stage, we projected the healthcare costs and disease burden [in disability-adjusted life-years (DALY)] in 2018-2040 under four treatment scenarios: (A) no treatment/baseline; (B) pre-2018 standard of care (pegylated interferon/ribavirin); (C) gradual scale-up in direct-acting antiviral (DAA) treatment uptake that does not meet the WHO 2030 treatment uptake target; (D) scale-up in DAA treatment uptake that meets the WHO 2030 target.

    RESULTS: Scenario D, while achieving the WHO 2030 target and averting 253,500 DALYs compared with the pre-2018 standard of care B, incurred the highest direct patient costs over the period 2018-2030: US$890 million (95% uncertainty interval 653-1271). When including screening programme costs, the total cost was estimated at US$952 million, which was 12% higher than the estimated total cost of scenario C.

    CONCLUSIONS: The scale-up to meet the WHO 2030 target may be achievable with appropriately high governmental commitment to the expansion of HCV screening to bring sufficient undiagnosed chronically infected patients into the treatment pathway.

    Matched MeSH terms: Forecasting
  8. Fulltext Incoming Work-In-Progress Prediction in Semiconductor Fabrication Foundry Using Long Short-Term Memory
    Tin TC, Chiew KL, Phang SC, Sze SN, Tan PS
    Comput Intell Neurosci, 2019;2019:8729367.
    PMID: 30719036 DOI: 10.1155/2019/8729367
    Preventive maintenance activities require a tool to be offline for long hour in order to perform the prescribed maintenance activities. Although preventive maintenance is crucial to ensure operational reliability and efficiency of the tool, long hour of preventive maintenance activities increases the cycle time of the semiconductor fabrication foundry (Fab). Therefore, this activity is usually performed when the incoming Work-in-Progress to the equipment is forecasted to be low. The current statistical forecasting approach has low accuracy because it lacks the ability to capture the time-dependent behavior of the Work-in-Progress. In this paper, we present a forecasting model that utilizes machine learning method to forecast the incoming Work-In-Progress. Specifically, our proposed model uses LSTM to forecast multistep ahead incoming Work-in-Progress prediction to an equipment group. The proposed model's prediction results were compared with the results of the current statistical forecasting method of the Fab. The experimental results demonstrated that the proposed model performed better than the statistical forecasting method in both hit rate and Pearson's correlation coefficient, r.
    Matched MeSH terms: Forecasting
  9. Fulltext Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning
    Cromwell EA, Osborne JCP, Unnasch TR, Basáñez MG, Gass KM, Barbre KA, et al.
    PLoS Negl Trop Dis, 2021 07;15(7):e0008824.
    PMID: 34319976 DOI: 10.1371/journal.pntd.0008824
    Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 0·71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50·2% exceed this threshold for suitability in at least one 5 × 5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify.
    Matched MeSH terms: Forecasting
  10. Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia
    Ng KY, Awang N
    Environ Monit Assess, 2018 Jan 06;190(2):63.
    PMID: 29306973 DOI: 10.1007/s10661-017-6419-z
    Frequent haze occurrences in Malaysia have made the management of PM10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM10 variation and good forecast of PM10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.
    Matched MeSH terms: Forecasting
  11. Implications of climate change damage for agriculture: sectoral evidence from Pakistan
    Ahmed A, Devadason ES, Al-Amin AQ
    Environ Sci Pollut Res Int, 2016 Oct;23(20):20688-20699.
    PMID: 27473615
    This paper gives a projection of the possible damage of climate change on the agriculture sector of Pakistan for the period 2012-2037, based on a dynamic approach, using an environment-related applied computable general equilibrium model (CGE). Climate damage projections depict an upward trend for the period of review and are found to be higher than the global average. Further, the damage to the agricultural sector exceeds that for the overall economy. By sector, climatic damage disproportionately affects the major and minor crops, livestock and fisheries. The largest losses following climate change, relative to the other agricultural sectors, are expected for livestock. The reason for this is the orthodox system of production for livestock, with a low adaptability to negative shocks of climate change. Overall, the findings reveal the high exposure of the agriculture sector to climate damage. In this regard, policymakers in Pakistan should take seriously the effects of climate change on agriculture and consider suitable technology to mitigate those damages.
    Matched MeSH terms: Forecasting
  12. Regression and multivariate models for predicting particulate matter concentration level
    Nazif A, Mohammed NI, Malakahmad A, Abualqumboz MS
    Environ Sci Pollut Res Int, 2018 Jan;25(1):283-289.
    PMID: 29032528 DOI: 10.1007/s11356-017-0407-2
    The devastating health effects of particulate matter (PM10) exposure by susceptible populace has made it necessary to evaluate PM10 pollution. Meteorological parameters and seasonal variation increases PM10 concentration levels, especially in areas that have multiple anthropogenic activities. Hence, stepwise regression (SR), multiple linear regression (MLR) and principal component regression (PCR) analyses were used to analyse daily average PM10 concentration levels. The analyses were carried out using daily average PM10 concentration, temperature, humidity, wind speed and wind direction data from 2006 to 2010. The data was from an industrial air quality monitoring station in Malaysia. The SR analysis established that meteorological parameters had less influence on PM10 concentration levels having coefficient of determination (R 2) result from 23 to 29% based on seasoned and unseasoned analysis. While, the result of the prediction analysis showed that PCR models had a better R 2 result than MLR methods. The results for the analyses based on both seasoned and unseasoned data established that MLR models had R 2 result from 0.50 to 0.60. While, PCR models had R 2 result from 0.66 to 0.89. In addition, the validation analysis using 2016 data also recognised that the PCR model outperformed the MLR model, with the PCR model for the seasoned analysis having the best result. These analyses will aid in achieving sustainable air quality management strategies.
    Matched MeSH terms: Forecasting
  13. Fulltext Modelling the Effectiveness of Epidemic Control Measures in Preventing the Transmission of COVID-19 in Malaysia
    Gill BS, Jayaraj VJ, Singh S, Mohd Ghazali S, Cheong YL, Md Iderus NH, et al.
    Int J Environ Res Public Health, 2020 Jul 30;17(15).
    PMID: 32751669 DOI: 10.3390/ijerph17155509
    Malaysia is currently facing an outbreak of COVID-19. We aim to present the first study in Malaysia to report the reproduction numbers and develop a mathematical model forecasting COVID-19 transmission by including isolation, quarantine, and movement control measures. We utilized a susceptible, exposed, infectious, and recovered (SEIR) model by incorporating isolation, quarantine, and movement control order (MCO) taken in Malaysia. The simulations were fitted into the Malaysian COVID-19 active case numbers, allowing approximation of parameters consisting of probability of transmission per contact (β), average number of contacts per day per case (ζ), and proportion of close-contact traced per day (q). The effective reproduction number (Rt) was also determined through this model. Our model calibration estimated that (β), (ζ), and (q) were 0.052, 25 persons, and 0.23, respectively. The (Rt) was estimated to be 1.68. MCO measures reduce the peak number of active COVID-19 cases by 99.1% and reduce (ζ) from 25 (pre-MCO) to 7 (during MCO). The flattening of the epidemic curve was also observed with the implementation of these control measures. We conclude that isolation, quarantine, and MCO measures are essential to break the transmission of COVID-19 in Malaysia.
    Matched MeSH terms: Forecasting
  14. Computers stand out at Tenth Population Census Conference
    Hearn RL
    Asian Pac Cens Forum, 1985 May;11(4):1-4, 9-14, 16.
    PMID: 12267276
    Matched MeSH terms: Forecasting*
  15. A climate distribution model of malaria transmission in Sudan
    Musa MI, Shohaimi S, Hashim NR, Krishnarajah I
    Geospat Health, 2012 Nov;7(1):27-36.
    PMID: 23242678
    Malaria remains a major health problem in Sudan. With a population exceeding 39 million, there are around 7.5 million cases and 35,000 deaths every year. The predicted distribution of malaria derived from climate factors such as maximum and minimum temperatures, rainfall and relative humidity was compared with the actual number of malaria cases in Sudan for the period 2004 to 2010. The predictive calculations were done by fuzzy logic suitability (FLS) applied to the numerical distribution of malaria transmission based on the life cycle characteristics of the Anopheles mosquito accounting for the impact of climate factors on malaria transmission. This information is visualized as a series of maps (presented in video format) using a geographical information systems (GIS) approach. The climate factors were found to be suitable for malaria transmission in the period of May to October, whereas the actual case rates of malaria were high from June to November indicating a positive correlation. While comparisons between the prediction model for June and the case rate model for July did not show a high degree of association (18%), the results later in the year were better, reaching the highest level (55%) for October prediction and November case rate.
    Matched MeSH terms: Forecasting/methods
  16. Recent and prospective population trends in Malaysia
    Jones GW, Tan PC
    J Southeast Asian Stud, 1985 Sep;16(2):262-80.
    PMID: 12267554
    Matched MeSH terms: Forecasting*
  17. Fulltext A machine learning approach of predicting high potential archers by means of physical fitness indicators
    Muazu Musa R, P P Abdul Majeed A, Taha Z, Chang SW, Ab Nasir AF, Abdullah MR
    PLoS One, 2019;14(1):e0209638.
    PMID: 30605456 DOI: 10.1371/journal.pone.0209638
    k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. Standard fitness measurements of the handgrip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were conducted. Multiple linear regression was utilised to ascertain the significant variables that affect the shooting score. It was demonstrated from the analysis that core muscle strength and vertical jump were statistically significant. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. The HACA clustered the archers into high potential archers (HPA) and low potential archers (LPA). The weighted k-NN outperformed all the tested models at itdemonstrated reasonably good classification on the evaluated indicators with an accuracy of 82.5 ± 4.75% for the prediction of the HPA and the LPA. Moreover, the performance of the classifiers was further investigated against fresh data, which also indicates the efficacy of the weighted k-NN model. These findings could be valuable to coaches and sports managers to recognise high potential archers from a combination of the selected few physical fitness performance indicators identified which would subsequently save cost, time and energy for a talent identification programme.
    Matched MeSH terms: Forecasting/methods*
  18. Is it worth repairing rheumatic mitral valve disease in children? Long-term outcomes of an aggressive approach to rheumatic mitral valve repair compared to replacement in young patients
    Krishna Moorthy PS, Sivalingam S, Dillon J, Kong PK, Yakub MA
    Interact Cardiovasc Thorac Surg, 2019 02 01;28(2):191-198.
    PMID: 30085022 DOI: 10.1093/icvts/ivy234
    OBJECTIVES: Contemporary experience in mitral valve (MV) repair for children with rheumatic heart disease (RHD) is limited, despite the potential advantages of repair over replacement. We reviewed our long-term outcomes of rheumatic MV repair and compared them with the outcomes of MV replacement in children with RHD.

    METHODS: This study is a review of 419 children (≤18 years) with RHD who underwent primary isolated MV surgery between 1992 and 2015, which comprised MV repair (336 patients; 80.2%) and MV replacement (83 patients; 19.8%). The replacement group included mechanical MV replacements (MMVRs) (n = 69 patients; 16.5%) and bioprosthetic MV replacements (n = 14 patients; 3.3%). The mean age with standard deviation at the time of operation was 12.5 ± 3.5 (2-18) years. Mitral regurgitation (MR) was predominant in 390 (93.1%) patients, and 341 (81.4%) patients showed ≥3+ MR. The modified Carpentier reconstructive techniques were used for MV repair.

    RESULTS: Overall early mortality was 1.7% (7 patients). The mean follow-up was 5.6 years (range 0-22.3 years; 94.7% complete). Survival of patients who underwent repair was 93.9% both at 10 and 20 years, which was superior than that of replacement (P 

    Matched MeSH terms: Forecasting*
  19. The birth prevalence, severity, and temporal trends of congenital heart disease in the middle-income country: A population-based study
    Mat Bah MN, Sapian MH, Jamil MT, Abdullah N, Alias EY, Zahari N
    Congenit Heart Dis, 2018 Nov;13(6):1012-1027.
    PMID: 30289622 DOI: 10.1111/chd.12672
    OBJECTIVES: There is limited data on congenital heart disease (CHD) from the lower- and middle-income country. We aim to study the epidemiology of CHD with the specific objective to estimate the birth prevalence, severity, and its trend over time.

    DESIGN: A population-based study with data retrieved from the Pediatric Cardiology Clinical Information System, a clinical registry of acquired and congenital heart disease for children.

    SETTING: State of Johor, Malaysia.

    PATIENTS: All children (0-12 years of age) born in the state of Johor between January 2006 and December 2015.

    INTERVENTION: None.

    OUTCOME MEASURE: The birth prevalence, severity, and temporal trend over time.

    RESULTS: There were 531,904 live births during the study period with 3557 new cases of CHD detected. Therefore, the birth prevalence of CHD was 6.7 per 1000 live births (LB) (95% confidence interval [CI]: 6.5-6.9). Of these, 38% were severe, 15% moderate, and 47% mild lesions. Hence, the birth prevalence of mild, moderate, and severe CHD was 3.2 (95% CI: 3.0-3.3), 0.9 (95% CI: 0.9- 1.1), and 2.6 (95% CI: 2.4-2.7) per 1000 LB, respectively. There was a significant increase in the birth prevalence of CHD, from 5.1/1000 LB in 2006 to 7.8/1000 LB in 2015 (P 
    Matched MeSH terms: Forecasting*
  20. A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
    Chun TS, Malek MA, Ismail AR
    Water Sci Technol, 2015;71(4):524-8.
    PMID: 25746643 DOI: 10.2166/wst.2014.451
    The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method - namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, non-linear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.
    Matched MeSH terms: Forecasting
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