Abstract
This paper is intended to apply data mining techniques for real Iraqi biochemical dataset to discover hidden patterns within tests relationships. It is worth noting that preprocessing steps take remarkable efforts to handle this type of data, since it is pure data set with so many null values reaching a ratio of 94.8%, then it becomes 0% after achieving these steps. However, in order to apply Classification And Regression Tree (CART) algorithm, several tests were assumed as classes, because of the dataset was unlabeled. Which then enabled discovery of patterns of tests relationships, that consequently, extends its impact on patients’ health, since it will assist in determining test values by performing only relevant tests. Therefore decreases the number of tests for patients.
Highlights
The Data Mining (DM) approach considered as an important and widespread field, because it provides useful results in several areas
The more obtainable biological data, the more interest in bioinformatics to analyze for more different types of emerged data
With more data offered, the task of bioinformatics evolved to analyze them in order to have new hidden knowledge, including chemical tests, protein domains, protein structures and so on [1]. 2
Summary
The Data Mining (DM) approach considered as an important and widespread field, because it provides useful results in several areas. It is easy to learn and provides many techniques that can be used in different ways. It is considered one of the useful sciences in life from which humanity benefits every day in many discoveries. The more obtainable biological data, the more interest in bioinformatics to analyze for more different types of emerged data. With more data offered, the task of bioinformatics evolved to analyze them in order to have new hidden knowledge, including chemical tests, protein domains, protein structures and so on [1].
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