Displaying publications 1 - 20 of 34 in total

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  1. Improving routine health data in Indonesia: Utilising the WHO data quality tool for Aplikasi Satu Data Kesehatan
    Sanjaya GY, Fauziah K, Pratama RA, Fitriani NA, Setiawan MY, Fauziah IA, et al.
    Med J Malaysia, 2024 Mar;79(2):176-183.
    PMID: 38553923
    INTRODUCTION: Assessment of data quality in the era of big data is crucial for effective data management and use. However, there are gaps in data quality assessment for routine health data to ensure accountability. Therefore, this research aims to improve the routine health data quality that have been collected and integrated into Aplikasi Satu Data Kesehatan (ASDK) as the primary health data system in Indonesia.

    MATERIALS AND METHODS: This descriptive study utilises a desk review approach and employs the WHO Data Quality Assurance (DQA) Tool to assess data quality of ASDK. The analysis involves measuring eight health indicators from ASDK and Survei Status Gizi Indonesia (SSGI) conducted in 2022. The assessment focuses on various dimensions of data quality, including completeness of variables, consistency over time, consistency between indicators, outliers and external consistency.

    RESULTS: Current study shows that routine health data in Indonesia performs high-quality data in terms of completeness and internal consistency. The dimension of data completeness demonstrates high levels of variable completeness with most variables achieving 100% of the completeness.

    CONCLUSION: Based on the analysis of eight routine health data variables using five dimensions of data quality namely completeness of variables, consistency over time, consistency between indicators, outliers. and external consistency. It shows that completeness and internal consistency of data in ASDK has demonstrated a high data quality.

    Matched MeSH terms: Data Accuracy*
  2. Fulltext A data fusion method in wireless sensor networks
    Izadi D, Abawajy JH, Ghanavati S, Herawan T
    Sensors (Basel), 2015;15(2):2964-79.
    PMID: 25635417 DOI: 10.3390/s150202964
    The success of a Wireless Sensor Network (WSN) deployment strongly depends on the quality of service (QoS) it provides regarding issues such as data accuracy, data aggregation delays and network lifetime maximisation. This is especially challenging in data fusion mechanisms, where a small fraction of low quality data in the fusion input may negatively impact the overall fusion result. In this paper, we present a fuzzy-based data fusion approach for WSN with the aim of increasing the QoS whilst reducing the energy consumption of the sensor network. The proposed approach is able to distinguish and aggregate only true values of the collected data as such, thus reducing the burden of processing the entire data at the base station (BS). It is also able to eliminate redundant data and consequently reduce energy consumption thus increasing the network lifetime. We studied the effectiveness of the proposed data fusion approach experimentally and compared it with two baseline approaches in terms of data collection, number of transferred data packets and energy consumption. The results of the experiments show that the proposed approach achieves better results than the baseline approaches.
    Matched MeSH terms: Data Accuracy
  3. Errors in accident data, its types, causes and methods of rectification-analysis of the literature
    Ahmed A, Sadullah AFM, Yahya AS
    Accid Anal Prev, 2019 Sep;130:3-21.
    PMID: 28764851 DOI: 10.1016/j.aap.2017.07.018
    Most of the decisions taken to improve road safety are based on accident data, which makes it the back bone of any country's road safety system. Errors in this data will lead to misidentification of black spots and hazardous road segments, projection of false estimates pertinent to accidents and fatality rates, and detection of wrong parameters responsible for accident occurrence, thereby making the entire road safety exercise ineffective. Its extent varies from country to country depending upon various factors. Knowing the type of error in the accident data and the factors causing it enables the application of the correct method for its rectification. Therefore there is a need for a systematic literature review that addresses the topic at a global level. This paper fulfils the above research gap by providing a synthesis of literature for the different types of errors found in the accident data of 46 countries across the six regions of the world. The errors are classified and discussed with respect to each type and analysed with respect to income level; assessment with regard to the magnitude for each type is provided; followed by the different causes that result in their occurrence, and the various methods used to address each type of error. Among high-income countries the extent of error in reporting slight, severe, non-fatal and fatal injury accidents varied between 39-82%, 16-52%, 12-84%, and 0-31% respectively. For middle-income countries the error for the same categories varied between 93-98%, 32.5-96%, 34-99% and 0.5-89.5% respectively. The only four studies available for low-income countries showed that the error in reporting non-fatal and fatal accidents varied between 69-80% and 0-61% respectively. The logistic relation of error in accident data reporting, dichotomised at 50%, indicated that as the income level of a country increases the probability of having less error in accident data also increases. Average error in recording information related to the variables in the categories of location, victim's information, vehicle's information, and environment was 27%, 37%, 16% and 19% respectively. Among the causes identified for errors in accident data reporting, Policing System was found to be the most important. Overall 26 causes of errors in accident data were discussed out of which 12 were related to reporting and 14 were related to recording. "Capture-Recapture" was the most widely used method among the 11 different methods: that can be used for the rectification of under-reporting. There were 12 studies pertinent to the rectification of accident location and almost all of them utilised a Geographical Information System (GIS) platform coupled with a matching algorithm to estimate the correct location. It is recommended that the policing system should be reformed and public awareness should be created to help reduce errors in accident data.
    Matched MeSH terms: Data Accuracy*
  4. Fulltext Unravelling the impact of dental workforce training and education programmes on policy evolution: a mixed-method study protocol
    Lin GSS, Goh SM, Halil MHM
    Health Res Policy Syst, 2023 Sep 12;21(1):95.
    PMID: 37700266 DOI: 10.1186/s12961-023-01048-9
    BACKGROUND: The dental workforce plays a crucial role in delivering quality oral healthcare services, requiring continuous training and education to meet evolving professional demands. Understanding the impact of dental workforce training and education programmes on policy evolution is essential for refining existing policies, implementing evidence-based reforms and ensuring the growth of the dental profession. Therefore, this study protocol aims to assess the influence of dental workforce training and education programmes on policy evolution in Malaysia.

    METHODS: A mixed-method research design will be employed, combining quantitative surveys and qualitative interviews. Stakeholder theory and policy change models will form the theoretical framework of the study. Participants from various stakeholder groups will be recruited using purposive sampling. Data collection will involve surveys and one-on-one semi-structured interviews. Descriptive statistics, inferential analysis and thematic analysis will be used to analyse the data. Integration of quantitative and qualitative data will be used to provide a comprehensive understanding of the data.

    DISCUSSION: This study will shed light on factors influencing policy decisions related to dental education and workforce development in Malaysia. The findings will inform evidence-based decision-making, guide the enhancement of dental education programmes and improve the quality of oral healthcare services. Challenges related to participant recruitment and data collection should be considered, and the study's unique contribution to the existing body of knowledge in the Malaysian context will be discussed.

    Matched MeSH terms: Data Accuracy*
  5. Fulltext Cross-sectional survey of multi-centre patient registries in malaysia
    Sharmini S, Jamaiyah H, Jaya Purany SP
    Malays Fam Physician, 2010;5(1):13-8.
    PMID: 25606180 MyJurnal
    This survey set out to describe patient registries available in the country, to determine their security features, data confidentiality, extent of outputs produced and data quality of the registries.
    Matched MeSH terms: Data Accuracy
  6. Fulltext Vision-Based Apple Classification for Smart Manufacturing
    Ismail A, Idris MYI, Ayub MN, Por LY
    Sensors (Basel), 2018 Dec 10;18(12).
    PMID: 30544660 DOI: 10.3390/s18124353
    Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN). The vision-based classifiers categorize the apple as defective and non-defective that can be used for automatic inspection, sorting and further analytics. A total of 550 apple images are collected to test the classifiers. The images consist of 275 non-defective and 275 defective apples. The defective category includes various types of defect and severity. The vision-based classifiers are trained and evaluated according to the K-fold cross-validation. The performances of the classifiers from 2-fold, 3-fold, 4-fold, 5-fold and 10-fold are compared. From the evaluation, SPM with SVM classifier attained 98.15% classification accuracy for 10-fold and outperformed the others. In terms of computational time, CNN with SVM classifier is the fastest. However, minimal time difference is observed between the computational time of CNN and SPM, which were separated by only 0.05 s.
    Matched MeSH terms: Data Accuracy
  7. Fulltext Life After Death in a Chinese Community
    MOHD HAFIZOL AMIN BIN RAMLI, NUR AMELIA BINTI MAZLAN, SITI AIDA BINTI AZMI, MUHD SARJI BIN AWANG BULAT, PARVEEN KAUR
    Trends in Undergraduate Research, 2018;1(1):0-0.
    MyJurnal
    According to San Fillipo (2006), death is not the end of one’s existence, but rather than a transition from one life to another. However, it is different based on how the society and individuals see the concept of death itself and how they understand about it. Thus, this article aims to explore the understanding of the relationship between culture and religion that become their identity in terms of death and life after. Qualitative approach is adopted for this study. Indeed, interview and empirical observation were used to obtain quality data.
    Matched MeSH terms: Data Accuracy
  8. Fulltext Development of smart wireless detector system for gamma ray detection
    Nolida Yussup, Nur Aira Abd. Rahman, Ismail Mustapha, Jaafar Abdullah, Mohd. Ashhar Khalid, Hearie Hassan, et al.
    Jurnal Sains Nuklear Malaysia, 2012;24(1):43-47.
    MyJurnal
    Data transmission in field works especially that is related to industry, gas and chemical is paramount importance to ensure data accuracy and delivery time. A development of wireless detector system for remote data acquisition to be applied in conducting fieldwork in industry is described in this paper. A wireless communication which is applied in the project development is a viable and cost-effective method of transmitting data from the detector to the laptop on the site to facilitate data storage and analysis automatically, which can be used in various applications such as column scanning. The project involves hardware design for the detector and electronics parts besides programming for control board and user interface. A prototype of a wireless gamma scintillation detector is developed with capabilities of transmitting data to computer via radio frequency (RF) and recording the data within the 433MHz band at baud rate of 19200.
    Matched MeSH terms: Data Accuracy
  9. A Comparison of Various Imputation Methods for Missing Values in Air Quality Data
    Nuryazmin Ahmat Zainuri, Abdul Aziz Jemain, Nora Muda
    Sains Malaysiana, 2015;44:449-456.
    This paper presents various imputation methods for air quality data specifically in Malaysia. The main objective was to
    select the best method of imputation and to compare whether there was any difference in the methods used between stations
    in Peninsular Malaysia. Missing data for various cases are randomly simulated with 5, 10, 15, 20, 25 and 30% missing.
    Six methods used in this paper were mean and median substitution, expectation-maximization (EM) method, singular
    value decomposition (SVD), K-nearest neighbour (KNN) method and sequential K-nearest neighbour (SKNN) method. The
    performance of the imputations is compared using the performance indicator: The correlation coefficient (R), the index
    of agreement (d) and the mean absolute error (MAE). Based on the result obtained, it can be concluded that EM, KNN
    and SKNN are the three best methods. The same result are obtained for all the eight monitoring station used in this study.
    Matched MeSH terms: Data Accuracy
  10. Application of functional data analysis for the treatment of missing air quality data
    Norshahida Shaadan, Sayang Mohd Deni, Abdul Aziz Jemain
    Sains Malaysiana, 2015;44:1531-1540.
    In most research including environmental research, missing recorded data often exists and has become a common problem for data quality. In this study, several imputation methods that have been designed based on the techniques for functional data analysis are introduced and the capability of the methods for estimating missing values is investigated. Single imputation methods and iterative imputation methods are conducted by means of curve estimation using regression and roughness penalty smoothing approaches. The performance of the methods is compared using a reference data set, the real PM10 data from an air quality monitoring station namely the Petaling Jaya station located at the western part of Peninsular Malaysia. A hundred of the missing data sets that have been generated from a reference data set with six different patterns of missing values are used to investigate the performance of the considered methods. The patterns are simulated according to three percentages (5, 10 and 15) of missing values with respect to two different sizes (3 and 7) of maximum gap lengths (consecutive missing points). By means of the mean absolute error, the index of agreement and the coefficient of determination as the performance indicators, the results have showed that the iterative imputation method using the roughness penalty approach is more flexible and superior to other methods.
    Matched MeSH terms: Data Accuracy
  11. Fulltext Feature Selection for High-Dimensional and Imbalanced Biomedical Data Based on Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm
    Abdulrauf Sharifai G, Zainol Z
    Genes (Basel), 2020 06 27;11(7).
    PMID: 32605144 DOI: 10.3390/genes11070717
    The training machine learning algorithm from an imbalanced data set is an inherently challenging task. It becomes more demanding with limited samples but with a massive number of features (high dimensionality). The high dimensional and imbalanced data set has posed severe challenges in many real-world applications, such as biomedical data sets. Numerous researchers investigated either imbalanced class or high dimensional data sets and came up with various methods. Nonetheless, few approaches reported in the literature have addressed the intersection of the high dimensional and imbalanced class problem due to their complicated interactions. Lately, feature selection has become a well-known technique that has been used to overcome this problem by selecting discriminative features that represent minority and majority class. This paper proposes a new method called Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm (rCBR-BGOA); rCBR-BGOA has employed an ensemble of multi-filters coupled with the Correlation-Based Redundancy method to select optimal feature subsets. A binary Grasshopper optimisation algorithm (BGOA) is used to construct the feature selection process as an optimisation problem to select the best (near-optimal) combination of features from the majority and minority class. The obtained results, supported by the proper statistical analysis, indicate that rCBR-BGOA can improve the classification performance for high dimensional and imbalanced datasets in terms of G-mean and the Area Under the Curve (AUC) performance metrics.
    Matched MeSH terms: Data Accuracy*
  12. Fulltext Modeling the Physical Properties of Gamma Alumina Catalyst Carrier Based on an Artificial Neural Network
    Majdi HS, Saud AN, Saud SN
    Materials (Basel), 2019 May 29;12(11).
    PMID: 31146451 DOI: 10.3390/ma12111752
    Porous γ-alumina is widely used as a catalyst carrier due to its chemical properties. These properties are strongly correlated with the physical properties of the material, such as porosity, density, shrinkage, and surface area. This study presents a technique that is less time consuming than other techniques to predict the values of the above-mentioned physical properties of porous γ-alumina via an artificial neural network (ANN) numerical model. The experimental data that was implemented was determined based on 30 samples that varied in terms of sintering temperature, yeast concentration, and socking time. Of the 30 experimental samples, 25 samples were used for training purposes, while the other five samples were used for the execution of the experimental procedure. The results showed that the prediction and experimental data were in good agreement, and it was concluded that the proposed model is proficient at providing high accuracy estimation data derived from any complex analytical equation.
    Matched MeSH terms: Data Accuracy
  13. The Kinetics of Enzyme Mixtures
    Brown S, Muhamad N, C Pedley K, C Simcock D
    Mol Biol Res Commun, 2014 Mar;3(1):21-32.
    PMID: 27843974
    Even purified enzyme preparations are often heterogeneous. For example, preparations of aspartate aminotransferase or cytochrome oxidase can consist of several different forms of the enzyme. For this reason we consider how different the kinetics of the reactions catalysed by a mixture of forms of an enzyme must be to provide some indication of the characteristics of the species present. Based on the standard Michaelis-Menten model, we show that if the Michaelis constants (Km) of two isoforms differ by a factor of at least 20 the steady-state kinetics can be used to characterise the mixture. However, even if heterogeneity is reflected in the kinetic data, the proportions of the different forms of the enzyme cannot be estimated from the kinetic data alone. Consequently, the heterogeneity of enzyme preparations is rarely reflected in measurements of their steady-state kinetics unless the species present have significantly different kinetic properties. This has two implications: (1) it is difficult, but not impossible, to detect molecular heterogeneity using kinetic data and (2) even when it is possible, a considerable quantity of high quality data is required.
    Matched MeSH terms: Data Accuracy
  14. Fulltext The importance of human capital perspective in the learning management system (LMS) decision making process at universities
    Norhaiza K, Rozainee K, Mohd Noor Abdul H, Phillip H, McGill T, Zainah Ahmad Z
    Jurnal Psikologi Malaysia, 2016;30:102-113.
    This research examined how managers in universities incorporate non-financial measures in their Learning Management Systems decision-making processes and particularly focused on the importance of the Human Capital perspective in LMS decision making processes. A mixed-methods approach to data collection was used involving both interviews and questionnaires. The qualitative data from the interviews were coded and analysed. A descriptive coding method using thematic analysis was used for the data coding. The qualitative data were analysed using an inductive approach where the categories of criteria and indicators were not determined before the interview. The participants in this research were five members of LMS decision-making teams at two different universities in Australia and 24 participants from different universities in Malaysia who were involved in LMS decision- making processes at their universities. The results of this research indicated that there was substantial support for using a multi-dimensional decision making model among IT decision makers at universities, particularly the Human Capital perspective and they believed that Human Capital measures are important and should be considered in a LMS decision making process.The research has both implications for theory and for practitioners where it contributes to the knowledge on LMS decision making in universities and IT decision making in general, and also in improving actual decision making practices.
    Matched MeSH terms: Data Accuracy
  15. Accurate and dynamic predictive model for better prediction in medicine and healthcare
    Alanazi HO, Abdullah AH, Qureshi KN, Ismail AS
    Ir J Med Sci, 2018 May;187(2):501-513.
    PMID: 28756541 DOI: 10.1007/s11845-017-1655-3
    INTRODUCTION: Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance.

    AIMS AND OBJECTIVES: In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life.

    CONCLUSION: The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

    Matched MeSH terms: Data Accuracy
  16. Fulltext Performance of women entrepreneurs: a result on formation of human capital and emulous factors
    Nor Azira Ayob, Sity Daud, Nurul Nadia Abu Hassan
    Malaysian Journal of Social Sciences and Humanities, 2018;3(3):173-188.
    MyJurnal
    Human resource development comprises skills, abilities, creativity and talent are amongst factors in
    human capital as well as emulous aspect. Hence, the emphasis on human capital development and
    emulous aspect is important to become a good leader for family, community and country. It is also
    important in ensuring entrepreneurs can compete in market economy today and they are able to meet
    customer demand. Thus, the objectives are the emphasis on the factors that are able to contribute in
    improving human capital and emulous of women. This is because, the right factor will enable the government to carry out in accordance with the factors that have been identified. In obtaining the
    factors contribute to human capital development, the survey method was conducted on 145 respondents
    among Bumiputera women entrepreneurs in Melaka state and supported with qualitative data from 10
    informants. The findings through exploratory factor analysis found that there are four main factors that
    contribute to human capital development among Bumiputera women entrepreneurs which are
    education and training, experience, social support and creativity, while three main factors that
    contribute to emulous among Bumiputera women entrepreneurs are financial assistance, facilities and
    infrastructure and commitment. Thus, the government is advised to emphasize on education and
    training as well as financial assistance to improve their abilities on human capital and emulous that is
    appropriate to support the women entrepreneurs need to increase their performance.
    Matched MeSH terms: Data Accuracy
  17. Fulltext Orphan drugs and high cost medications: challenges and solutions
    Chuah, S.Y., Thong, M.K.
    JUMMEC, 2018;21(2):53-58.
    MyJurnal
    There had been increased and strong public interests in rare diseases and orphan drugs as well as the issue of
    compulsory licencing for expensive medications in Malaysia in the mass-media and social media. We reviewed
    the issues of orphan drugs and the challenges faced in many countries in developing appropriate health financial
    modelling as well as getting accurate data on rare diseases. We also reviewed the old off-patent medications
    and the developments on how policy-makers can intervene to make expensive treatment affordable and
    sustainable for patients and the country.
    Matched MeSH terms: Data Accuracy
  18. Evaluation of Development Datasets for Hermetia illucens (L.) (Diptera: Stratiomyidae) for Estimating the Time of Placement of Human and Swine Remains in Texas, USA
    Cuttiford L, Pimsler ML, Heo CC, Zheng L, Karunaratne I, Trissini G, et al.
    J Med Entomol, 2021 07 16;58(4):1654-1662.
    PMID: 33970239 DOI: 10.1093/jme/tjab081
    A basic tenet of forensic entomology is development data of an insect can be used to predict the time of colonization (TOC) by insect specimens collected from remains, and this prediction is related to the time of death and/or time of placement (TOP). However, few datasets have been evaluated to determine their accuracy or precision. The black soldier fly, Hermetia illucens (L.) (Diptera: Stratiomyidae) is recognized as an insect of forensic importance. This study examined the accuracy and precision of several development datasets for the black soldier fly by estimating the TOP of five sets of human and three sets of swine remains in San Marcos and College Station, TX, respectively. Data generated from this study indicate only one of these datasets consistently (time-to-prepupae 52%; time-to-eclosion 75%) produced TOP estimations that occurred within a day of the actual TOP of the remains. It is unknown if the precolonization interval (PreCI) of this species is long, but it has been observed that the species can colonize within 6 d after death. This assumption remains untested by validation studies. Accounting for this PreCI improved accuracy for the time-to-prepupae group, but reduced accuracy in the time-to-eclosion group. The findings presented here highlight a need for detailed, forensic-based development data for the black soldier fly that can reliably and accurately be used in casework. Finally, this study outlines the need for a basic understanding of the timing of resource utilization (i.e., duration of the PreCI) for forensically relevant taxa so that reasonable corrections may be made to TOC as related to minimum postmortem interval (mPMI) estimates.
    Matched MeSH terms: Data Accuracy
  19. Fulltext Pattern Matching for DNA Sequencing Data Using Multiple Bloom Filters
    Najam M, Rasool RU, Ahmad HF, Ashraf U, Malik AW
    Biomed Res Int, 2019;2019:7074387.
    PMID: 31111064 DOI: 10.1155/2019/7074387
    Storing and processing of large DNA sequences has always been a major problem due to increasing volume of DNA sequence data. However, a number of solutions have been proposed but they require significant computation and memory. Therefore, an efficient storage and pattern matching solution is required for DNA sequencing data. Bloom filters (BFs) represent an efficient data structure, which is mostly used in the domain of bioinformatics for classification of DNA sequences. In this paper, we explore more dimensions where BFs can be used other than classification. A proposed solution is based on Multiple Bloom Filters (MBFs) that finds all the locations and number of repetitions of the specified pattern inside a DNA sequence. Both of these factors are extremely important in determining the type and intensity of any disease. This paper serves as a first effort towards optimizing the search for location and frequency of substrings in DNA sequences using MBFs. We expect that further optimizations in the proposed solution can bring remarkable results as this paper presents a proof of concept implementation for a given set of data using proposed MBFs technique. Performance evaluation shows improved accuracy and time efficiency of the proposed approach.
    Matched MeSH terms: Data Accuracy
  20. Audit on data accuracy of the Malaysian Dialysis and Transplant Registry (MDTR)
    Thong KM, Jalalonmuhali M, Choo CL, Yee SY, Yahya R, Jeremiah PN, et al.
    Med J Malaysia, 2024 Mar;79(2):234-236.
    PMID: 38553931
    Diabetes mellitus is the main aetiology of end stage kidney disease (ESKD) in Malaysia. However, there may be concerns of over-reporting of diabetes mellitus as the cause of ESKD in the Malaysian Dialysis and Transplant Registry (MDTR). The objective of this audit is to assess the accuracy of data collected in the MDTR. There were 151 centres/source data providers (SDP) with a total of 1977 patients included in this audit. The audit showed that 80.2% of doctors' records matched the MDTR data. The results were comparable with published validation studies in other countries.
    Matched MeSH terms: Data Accuracy
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