Essay: The need to eliminate the missing record in the database

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  • Subject area(s): Information technology essays
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  • Published on: February 9, 2016
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  • The need to eliminate the missing record in the database
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To enhance or make a new model that can make functional relationships between covariates and response variables. This model should work in a feedforward manner so that mean error is reached to threshold after finite no. of iteration [7].
Databases are complex structures that may conceal implicit patterns of information that cannot be easily discovered by conventional analysis and interrogation methods. This paper describes the use of neural network techniques used in an ongoing knowledge discovery exercise applied to one such database. The ovulation induction infertility database at the “NOT MENTIONED HOSPITAL”, holds details of patients treated with gonadotrophins for ovulation induction. The data held is multidimensional in nature, and is of a level of complexity such that it is currently very difficult to predict with some degree of certainty, the outcome of a particular treatment cycle (i.e. the probability of a patient becoming pregnant) [8].
1.7 Organization of Thesis
This research work was done based on the need to eliminate the missing record in the database. The data used in this research was obtained from the educational sector, the work consists of five chapters. Chapter one include introduction, we discuss on infertility, the causes of infertility in male and females, definition and need of artificial neural networks and problem statement. Literature review in chapter two. Proposed methodology in chapter three. Implementation, result and discussion are explained in chapter four. Conclusion and references in chapter five.
To enhance or make a new model that can make functional relationships between covariates and response variables. This model should work in a feedforward manner so that mean error is reached to threshold after finite no. of iteration [7].
Databases are complex structures that may conceal implicit patterns of information that cannot be easily discovered by conventional analysis and interrogation methods. This paper describes the use of neural network techniques used in an ongoing knowledge discovery exercise applied to one such database. The ovulation induction infertility database at the “NOT MENTIONED HOSPITAL”, holds details of patients treated with gonadotrophins for ovulation induction. The data held is multidimensional in nature, and is of a level of complexity such that it is currently very difficult to predict with some degree of certainty, the outcome of a particular treatment cycle (i.e. the probability of a patient becoming pregnant) [8].
1.7 Organization of Thesis
This research work was done based on the need to eliminate the missing record in the database. The data used in this research was obtained from the educational sector, the work consists of five chapters. Chapter one include introduction, we discuss on infertility, the causes of infertility in male and females, definition and need of artificial neural networks and problem statement. Literature review in chapter two. Proposed methodology in chapter three. Implementation, result and discussion are explained in chapter four. Conclusion and references in chapter five.

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