Browsing by Author "Nor Azlida Aleng"

Now showing 1 - 5 of 5
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    Application Of Radial Basis Function Network On Parkinson Data
    (Jurnal Teknologi, 2015-10) Nur Farahana Zainudin; Norizan Mohamed; Nor Azlida Aleng; Siti Hasliza Ahmad Rusmili
    Radial basis function networks have many uses, including the function approximation, time series production, classification and system control. Radial basis function based diagnosis of medical diseases has been taken into great consideration in recent studies. The real data from UCI Machine Learning websites that used 500 Parkinson’s patients and 7 different attributes as the subject were analyzed by using Statistical Package for Social Sciences (SPSS) 21.0. Next, the result of SPSS software will be used and run by MATLAB software. From the research that has been done by other researchers, it was found that MATLAB software is much better in producing the best results for Radial Basis Function. The value of R2 for Multiple Linear Regression and Radial Basis Function is 0.7450 and 0.9702 respectively. Hence, the Radial Basis Function method shows that there is more variability is explained by this model.
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    Box-Cox Transfromation and Bootstrapping Approach to One Sample T-Test
    (World Applied Sciences Journal, 2015) Wan Muhamad Amir W Ahmad; Syerrina Binti Zakaria; Nor Azlida Aleng; Nurfadhlina Abdul Halim; Zalila Ali
    One sample t-test is one of the most popular collections of statistical technique for analyzing data. Before we perform one sample T-Test the first thing that we should check is normality assumption. In this paper, we combine Box-Cox and bootstrapping idea in one algorithm. The purpose of Box-Cox is to ensure the data is normally distributed before the analysis. This combination is very useful for the modelling with an advanced analysis and perhaps can be an alternative method for modelling options in applied statistics scope. Through this combining method, we are capable to handle the case of non-normal data and small and limited sample size data by bootstrapping the original data set to generate new ones. In our case, the term “bootstrap” actually is referring to the use of the original data set to generate new ones. In this research paper, from a small and limited sample size data, we performed bootstrapping method in order to generate a new data set with a bigger sample size. After getting a new sample size, we then perform one sample T-Test using standard procedures and modified procedure. Results from both analyses will be compared with others to know the efficiency of the modified procedure. We also provided some example of application of the method discussed by using SAS language computer software.
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    Canonical Correspondence Analysis and path analysis model (CCA) applied to dental caries among children six and seven year olds, bachok, kelantan, malaysia
    (International Journal Of Advanced Research, 2016-05) Ruhaya Hasan; Wan Muhamad Amir W Ahmad; Nor Affendy Nor Azmi; Rosmaliza Ramli; Nurhafizah Ghani; Zailani Abdullah; Nor Azlida Aleng; Nurfadhlina Abdul Halim; Syerrina Zakaria; Kasypi Mokhtar
    A correspondence analysis and path analysis approach were conducted for a dental caries case study among 6-7 year-old children from (Bachok District) Kelantan. Data from382 children were collected and caries status was examined visually by two dental officers from School of Dental Sciences, Hospital Universiti Sains Malaysia (HUSM). All related and important information observed was recorded in a research form. Results showed that 63.1% of the children was in high caries category. The incidence of caries among these children was very high and required attention from the government. The factors associated with caries status were analyzed using graphical mapping approach and path model analysis. This statistical technique facilitated the visualization through mapping procedure and path modeling analysis of the studied variables. All data were processed and analyzed using SAS (for Canonical Correspondence Analysis) and SPSS (Logistic Regression) software.
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    Modeling Medical Data Using MM-Estimation Applied to Body Mass Index Data.
    (Applied Mathematical Sciences, 2015) Nor Azlida Aleng
    In medical statistics research, there are many methodologies used to investigate and to model the relationship between two or more variables. A model is often not useful when its fails to fit the data and the outliers may exist. Outliers play important role in regression. An outliers (observations) that is quite different from most the other values or observations in a data set. Robust regression is the most popular method that has been used to detect outliers and to provide resistant results in the presence of outliers in the data set. The purpose of this study is to show that, robust MM-estimation is an alternative approach in dealing with outliers presence in the medical data. This approach is extremely useful in identifying outliers and assessing the adequacy of a fitted model
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    A study of an efficiency of handling overdispersion using poisson regression and zero inflated poisson regression
    (Journal of Advanced Scientific Research, 2015) Wan Muhamad Amir W Ahmad; Nur Syabiha Zafakali; Nurfadhlina Halim; Nor Azlida Aleng; Syerrina Zakaria
    Thalassemia is a blood related illness though descendant. Increasing number of patients suffering from thalassemia, especially among children has been reported year by year and has been identified ahead of other hereditary diseases in many parts of the world especially in Malaysia. Therefore, the increasing numbers of this illness annually, attracts the interest among the researchers to put an extra effort in order to overcome this illness. Other than that, most patients were also exposed other chronic diseases such as hemorrhagic illness, health problems, heart failure, influenza, anemia, pneumonia, acute bronchitis, asthma, acute tonsillitis, jaundice and tuberculosis. Data from patients especially among children has been successfully collected and it has been manifested in the form of statistic for analysis.