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With the advent of big data in the last two decades, in order to exploit and use this type of data, the need to integrate databases for building a stronger evidence base for policy and service development is felt more than ever. Therefore, familiarity with the methodology of data linkage as one of the methods of data integration and the use of machine learning methods to facilitate the process of recording records is essential. In this paper, in addition to introducing the record linkage process and some related methods, machine learning algorithms are required to increase the speed of database integration, reduce costs and improve record linkage performance. In this paper, two databases of the Statistical Center of Iran and Social Security Organization are linked.

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Non-invasive NIPT test has been used in trisomy 21 screening. However, there is a possibility of misdiagnosis in the methods used to diagnose Down syndrome. Therefore, it is essential to provide a process that can be used alongside these methods to improve efficiency. The main goal of this article is to design a model based on machine learning algorithms for the early diagnosis of Down syndrome. Machine learning algorithms such as support vector machine, simple Bayes, decision tree, random forest, and nearest neighbor, which are frequently used to improve the diagnosis of disorders, have been implemented on the mentioned dataset. The performance of each model on the Down syndrome dataset was investigated, and the most suitable model for this purpose was introduced.

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Researchers often face the problem of how to address missing data. Multiple imputation by chained equations is one of the most common methods for imputation. In theory, any imputation model can be used to predict the missing values. However, if the predictive models are incorrect, it can lead to biased estimates and invalid inferences. One of the latest solutions for dealing with missing data is machine learning methods and the SuperMICE method. In this paper, We present a set of simulations indicating that this approach produces final parameter estimates with lower bias and better coverage than other commonly used imputation methods. Also, implementing some machine learning methods and an ensemble algorithm, SuperMICE, on the data of the Industrial establishment survey is discussed,  in which the imputation of different variables in the data co-occurs. Also, the evaluation of various methods is discussed, and the method that has better performance than the other methods is introduced.